THE PERPETUAL LINE-UP
UNREGULATED POLICE FACE RECOGNITION IN AMERICA
www.perpetuallineup.org
OCTOBER 18, 2016
THE
PERPETUAL
LINE-UP
UNREGULATED POLICE FACE RECOGNITION IN AMERICA
Clare Garvie, Associate
Alvaro M. Bedoya, Executive Director
Jonathan Frankle, Sta Technologist
RESEARCH
Moriah Daugherty, Research Assistant
Katie Evans, Associate
Edward J. George, Chief Research Assistant
Sabrina McCubbin, Research Assistant
Harrison Rudolph, Law Fellow
Ilana Ullman, Google Policy Fellow
Sara Ainsworth, Research Assistant
David Houck, Research Assistant
Megan Iorio, Research Assistant
Matthew Kahn, Research Assistant
Eric Olson, Research Assistant
Jaime Petenko, Research Assistant
Kelly Singleton, Research Assistant
DESIGN
Rootid
www.perpetuallineup.org
OCTOBER 18, 2016
WALT WHITMAN
Writing and talk do not prove me,
I carry the plenum of proof and every thing else in my face…
I. EXECUTIVE SUMMARY 1
A. Key Findings 2
B. Recommendations 4
II. INTRODUCTION 7
III. BACKGROUND 9
A. What is Face Recognition Technology? 9
B. e Unique Risks of Face Recognition 9
C. How Does Law Enforcement Use Face Recognition? 10
D. Our Research 12
IV. A RISK FRAMEWORK FOR LAW ENFORCEMENT FACE RECOGNITION 16
A. Risk Factors 16
B. Risk Framework 17
V. FINDINGS & SCORECARD 23
A. Deployment 23
B. Fourth Amendment 31
C. Free Speech 41
D. Accuracy 46
E. Racial Bias 53
F. Transparency & Accountability 58
VI. RECOMMENDATIONS 62
A. Legislatures 62
B. Law Enforcement 65
C. e National Institute for Standards & Technology 68
D. Face Recognition Companies 69
E. Community Leaders 70
VII. CONCLUSION 72
VIII. ACKNOWLEDGEMENTS 74
IX. ENDNOTES 75
X. METHODOLOGY 93
XI. MODEL FACE RECOGNITION LEGISLATION 102
XII. MODEL POLICE FACE RECOGNITION USE POLICY 116
XIII. CITY & STATE BACKGROUNDERS 120
TABLE OF CONTENTS
1
ere is a knock on your door. It’s the police.
ere was a robbery in your neighborhood. ey
have a suspect in custody and an eyewitness. But
they need your help: Will you come down to the
station to stand in the line-up?
Most people would probably answer “no.” is
summer, the Government Accountability Oce
revealed that close to 64 million Americans do
not have a say in the matter: 16 states let the
FBI use face recognition technology to compare
the faces of suspected criminals to their driver’s
license and ID photos, creating a virtual line-up
of their state residents. In this line-up, it’s not a
human that points to the suspect—it’s an algorithm.
But the FBI is only part of the story. Across the
country, state and local police departments are
building their own face recognition systems,
many of them more advanced than the FBI’s.
We know very little about these systems. We
don’t know how they impact privacy and civil
liberties. We don’t know how they address
accuracy problems. And we don’t know how any
of these systems—local, state, or federal—aect
racial and ethnic minorities.
is report closes these gaps. e result of a year-
long investigation and over 100 records requests
to police departments around the country, it is
the most comprehensive survey to date of law
enforcement face recognition and the risks that
it poses to privacy, civil liberties, and civil rights.
Combining FBI data with new information we
obtained about state and local systems, we nd
that law enforcement face recognition aects
over 117 million American adults. It is also
unregulated. A few agencies have instituted
meaningful protections to prevent the misuse
of the technology. In many more cases, it is out of
control.
One in two American adults
is in a law enforcement face
recognition network.
e benets of face recognition are real. It
has been used to catch violent criminals and
fugitives. e law enforcement ocers who use
the technology are men and women of good
faith. ey do not want to invade our privacy or
create a police state. ey are simply using every
tool available to protect the people that they are
sworn to serve. Police use of face recognition is
inevitable. is report does not aim to stop it.
Rather, this report oers a framework to reason
through the very real risks that face recognition
creates. It urges Congress and state legislatures
to address these risks through commonsense
regulation comparable to the Wiretap Act. ese
reforms must be accompanied by key actions
by law enforcement, the National Institute
of Standards and Technology (NIST), face
recognition companies, and community leaders.
I. EXECUTIVE SUMMARY
2
The Perpetual Line-Up
A. KEY FINDINGS
Our general ndings are set forth below. Specic
ndings for 25 local and state law enforcement
agencies can be found in the City & State
Backgrounders (p. 120). Our Face Recognition
Scorecard (p. 24) evaluates these agencies’ impact
on privacy, civil liberties, civil rights, transparency
and accountability. e records underlying all of
our conclusions are available online.
1. Law enforcement face recognition
networks include over 117 million
American adults—and may soon include
many more. Face recognition is neither new
nor rare. FBI face recognition searches are
more common than federal court-ordered
wiretaps. At least one out of four state or
local police departments has the option to
run face recognition searches through their
or another agency’s system. At least 26
states (and potentially as many as 30) allow
law enforcement to run or request searches
against their databases of driver’s license and
ID photos. Roughly one in two American
adults has their photos searched this way.
2. Dierent uses of face recognition
create dierent risks. is report oers
a framework to tell them apart. A face
recognition search conducted in the eld
to verify the identity of someone who has
been legally stopped or arrested is dierent,
in principle and eect, than an investigatory
search of an ATM photo against a driver’s
license database, or continuous, real-time
scans of people walking by a surveillance
camera. e former is targeted and public.
e latter are generalized and invisible.
While some agencies, like the San Diego
Association of Governments, limit
themselves to more targeted use of the
technology, others are embracing high and
very high risk deployments.
3. By tapping into driver’s license databases,
the FBI is using biometrics in a way
it’s never done before. Historically, FBI
ngerprint and DNA databases have
been primarily or exclusively made up
of information from criminal arrests or
investigations. By running face recognition
searches against 16 states’ driver’s license
photo databases, the FBI has built a
biometric network that primarily includes
law-abiding Americans. is is unprecedented
and highly problematic.
4. Major police departments are exploring
real-time face recognition on live
surveillance camera video. Real-time
face recognition lets police continuously
scan the faces of pedestrians walking by a
street surveillance camera. It may seem like
science ction. It is real. Contract documents
and agency statements show that at least
ve major police departments—including
agencies in Chicago, Dallas, and Los
Angeles—either claimed to run real-time
face recognition o of street cameras, bought
technology that can do so, or expressed
an interest in buying it. Nearly all major
face recognition companies oer real-time
software.
5. Law enforcement face recognition is
unregulated and in many instances out
of control. No state has passed a law
comprehensively regulating police face
recognition. We are not aware of any
agency that requires warrants for searches
or limits them to serious crimes. is
has consequences. e Maricopa County
Sheri ’s Oce enrolled all of Honduras’
driver’s licenses and mug shots into its
database. e Pinellas County Sheri ’s
Oce system runs 8,000 monthly searches
on the faces of seven million Florida
3
drivers—without requiring that ocers have
even a reasonable suspicion before running
a search. e county public defender reports
that the Sheri ’s Oce has never disclosed
the use of face recognition in Brady evidence.
6. Most law enforcement agencies are not
taking adequate steps to protect free
speech. ere is a real risk that police face
recognition will be used to stie free speech.
ere is also a history of FBI and police
surveillance of civil rights protests. Of the
52 agencies that we found to use (or have
used) face recognition, we found only one,
the Ohio Bureau of Criminal Investigation,
whose face recognition use policy expressly
prohibits its ocers from using face
recognition to track individuals engaging in
political, religious, or other protected free
speech.
7. Most law enforcement agencies do little
to ensure that their systems are accurate.
Face recognition is less accurate than
ngerprinting, particularly when used in
real-time or on large databases. Yet we found
only two agencies, the San Francisco Police
Department and the Seattle region’s South
Sound 911, that conditioned purchase of the
technology on accuracy tests or thresholds.
ere is a need for testing. One major face
recognition company, FaceFirst, publicly
advertises a 95% accuracy rate but disclaims
liability for failing to meet that threshold in
contracts with the San Diego Association of
Governments. Unfortunately, independent
accuracy tests are voluntary and infrequent.
8. e human backstop to accuracy is non-
standardized and overstated. Companies and
police departments largely rely on police ocers
to decide whether a candidate photo is in fact a
match. Yet a recent study showed that, without
specialized training, human users make the
wrong decision about a match half the time. We
found only eight face recognition systems where
specialized personnel reviewed and narrowed
down potential matches. e training regime
for examiners remains a work in progress.
9. Police face recognition will
disproportionately aect African
Americans. Many police departments do
not realize that. In a Frequently Asked
Questions document, the Seattle Police
Department says that its face recognition
system “does not see race.” Yet an FBI co-
authored study suggests that face recognition
may be less accurate on black people. Also,
due to disproportionately high arrest rates,
systems that rely on mug shot databases likely
include a disproportionate number of African
Americans. Despite these ndings, there is no
independent testing regime for racially biased
error rates. In interviews, two major face
recognition companies admitted that they did
not run these tests internally, either.
Face recognition may be
least accurate for those it is
most likely to aect:
African Americans
10. Agencies are keeping critical information
from the public. Ohio’s face recognition
system remained almost entirely unknown
to the public for ve years. e New York
Police Department acknowledges using face
recognition; press reports suggest it has an
advanced system. Yet NYPD denied our
records request entirely. e Los Angeles
Police Department has repeatedly announced
4
The Perpetual Line-Up
new face recognition initiatives—including a
“smart car” equipped with face recognition and
real-time face recognition cameras—yet the
agency claimed to have “no records responsive”
to our document request. Of 52 agencies, only
four (less than 10%) have a publicly available
use policy. And only one agency, the San
Diego Association of Governments, received
legislative approval for its policy.
11. Major face recognition systems are not
audited for misuse. Maryland’s system,
which includes the license photos of over
two million residents, was launched in 2011.
It has never been audited. e Pinellas
County Sheri ’s Oce system is almost 15
years old and may be the most frequently
used system in the country. When asked if
his oce audits searches for misuse, Sheri
Bob Gualtieri replied, “No, not really.”
Despite assurances to Congress, the FBI
has not audited use of its face recognition
system, either. Only nine of 52 agencies
(17%) indicated that they log and audit
their ocers’ face recognition searches for
improper use. Of those, only one agency,
the Michigan State Police, provided
documentation showing that their audit
regime was actually functional.
B. RECOMMENDATIONS
1. Congress and state legislatures should
pass commonsense laws to regulate law
enforcement face recognition. Such laws
should require the FBI or the police to have a
reasonable suspicion of criminal conduct prior
to a face recognition search. After-the-fact
investigative searches—which are invisible to
the public—should be limited to felonies.
Mug shots, not driver’s license and ID
photos, should be the default photo
databases for face recognition, and they
should be periodically scrubbed to eliminate
the innocent. Except for identity theft and
fraud cases, searches of license and ID
photos should require a court order issued
upon a showing of probable cause, and
should be restricted to serious crimes. If
these searches are allowed, the public should
be notied at their department of motor
vehicles.
If deployed pervasively on surveillance video
or police-worn body cameras, real-time face
recognition will redene the nature of public
spaces. At the moment, it is also inaccurate.
Communities should carefully weigh whether
to allow real-time face recognition. If they do,
it should be used as a last resort to intervene
in only life-threatening emergencies. Orders
allowing it should require probable cause,
specify where continuous scanning will occur,
and cap the length of time it may be used.
Real-time face recognition
will redene the nature of
public spaces. It should be
strictly limited.
Use of face recognition to track people on
the basis of their political or religious beliefs
or their race or ethnicity should be banned.
All face recognition use should be subject to
public reporting and internal audits.
To lay the groundwork for future
improvements in face recognition, Congress
5
should provide funding to NIST to increase
the frequency of accuracy tests, create
standardized, independent testing for racially
biased error rates, and create photo databases
that facilitate such tests.
State and federal nancial assistance for police
face recognition systems should be contingent
on public reporting, accuracy and bias tests,
legislative approval—and public posting—
of a face recognition use policy, and other
standards in line with these recommendations.
A Model Face Recognition Act (p. 102), for
Congress or a state legislature, is included at
the end of the report.
2. e FBI and Department of Justice (DOJ)
should make signicant reforms to the
FBI’s face recognition system. e FBI
should refrain from searching driver’s license
and ID photos in the absence of express
approval for those searches from a state
legislature. If it proceeds with those searches,
the FBI should restrict them to investigations
of serious crimes where FBI ocials have
probable cause to implicate the search
subject. e FBI should periodically scrub its
mug shot database to eliminate the innocent,
require reasonable suspicion for state searches
of that database, and restrict those searches
to investigations of felonies. Overall access
to the database should be contingent on
legislative approval of an agency’s use policy.
e FBI should audit all searches for misuse,
and test its own face recognition system, and
the state systems that the FBI accesses, for
accuracy and racially biased error rates.
e DOJ Civil Rights Division should
evaluate the disparate impact of police face
recognition, rst in jurisdictions where it has
open investigations and then in state and
local law enforcement more broadly. DOJ
should also develop procurement guidance
for state and local agencies purchasing face
recognition programs with federal funding
e FBI should be transparent about its
use of face recognition. It should reverse
its current proposal to exempt its face
recognition system from key Privacy Act
requirements. It should also publicly and
annually identify the photo databases
it searches and release statistics on the
number and nature of searches, arrests, the
convictions stemming from those searches,
and the crimes that those searches were used
to investigate.
3. Police should not run face recognition
searches of license photos without
clear legislative approval. Many police
departments have run searches of driver’s
license and ID photos without express
legislative approval. Police should observe a
moratorium on those searches until legislatures
vote on whether or not to allow them.
Police should develop use policies for face
recognition, publicly post those policies, and
seek approval for them from city councils
or other local legislative bodies. City
councils should involve their communities
in deliberations regarding support for this
technology, and consult with privacy and
civil liberties organizations in reviewing
proposed use policies.
When buying software and hardware, police
departments should condition purchase on
accuracy and bias tests and periodic tests of
the systems in operational conditions over
the contract period. ey should avoid sole
source contracts and contracts that disclaim
vendor responsibility for accuracy.
6
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All agencies should implement audits to
prevent and identify misuse and a system of
trained face examiners to maximize accuracy.
Regardless of their approach to contracting,
all agencies should regularly test their
systems for accuracy and bias.
A Model Police Face Recognition Use
Policy (p.116) is included at the end of this
report.
4. e National Institute of Standards and
Technology (NIST) should expand the
scope and frequency of accuracy tests.
NIST should create regular tests for
algorithmic bias on the basis of race, gender,
and age, increase the frequency of existing
accuracy tests, develop tests that mirror law
enforcement workows, and deepen its focus
on tests for real-time face recognition. To
help empower others to conduct testing,
NIST should develop a set of best practices
for accuracy tests and develop and distribute
new photo datasets to train and evaluate
algorithms. To help eorts to diminish
racially biased error rates, NIST should
ensure that these datasets reect the diversity
of the American population.
5. Face recognition companies should test
their systems for algorithmic bias on the
basis of race, gender, and age. Companies
should also voluntarily publish performance
results for modern, publicly available
benchmarks—giving police departments and
city councils more bases upon which to draw
comparisons.
6. Community leaders should press for
policies and legislation that protect privacy,
civil liberties, and civil rights. Citizens are
paying for police and FBI face recognition
systems. ey have a right to know how
those systems are being used.
If those agencies refuse, advocates should
take them to court. Citizens should also
press legislators and law enforcement
agencies for laws and use policies that
protect privacy, civil liberties, and civil
rights, and prevent misuse and abuse. Law
enforcement and legislatures will not act
without concerted community action.
is report provides the resources that citizens
will need to eect this change. In addition
to the City and State Backgrounders and
the Face Recognition Scorecard, a list of
questions that citizens can ask their elected
representative or law enforcement agency is in
the Recommendations (p. 70).
7
Chris Wilson is a soft-spoken Classics major
working towards her second Bachelor’s degree
at the University of South Florida. She enjoys
learning Latin and studying ancient Greece and
Rome. “I’m a history nut,” she says.
But Chris is not just a scholar—she is also a
civil rights leader. For her, social justice is at
the core of education: “A lot of students believe
that we have to put up with the way things
are—and that’s not right.” Chris sees it as her
responsibility to “pop the bubble.”
Earlier this year, Chris helped organize a protest
against the treatment of black students at the
Florida State Fair. In 2014, a 14 year-old honors
student, Andrew Joseph III, had been killed by
a passing car after being ejected by police from
the Florida State Fair along with dozens of other
students, most of them African American.
1
On February 7, 2016—the second anniversary of
Andrew’s death—Chris and three others locked
themselves together just inside the fairground
gates and called for a boycott. Police ordered them
II. INTRODUCTION
Figure 1: Chris Wilson at the University of South Florida campus. (Photo: Center on Privacy & Technology)
8
The Perpetual Line-Up
to leave. Chris and her friends stayed where they
were.
2
Chris was arrested for trespass, a misdemeanor.
e Hillsborough County Sheri ’s Oce took
her to a local station, ngerprinted her, took
her mug shot, and released her that evening.
She had never been arrested before, and so
she was informed that she was eligible for a
special diversion program. She paid a ne, did
community service, and the charges against her
were dropped.
Chris was not told that as a result of her arrest,
her mug shot has likely been added to not one,
but two separate face recognition databases run
by the FBI and the Pinellas County Sheri ’s
Oce.
3
ese two databases alone are searched
thousands of times a year by over 200 state, local
and federal law enforcement agencies.
4
e next time Chris participates in a protest, the
police won’t need to ask her for her name in order
to identify her. ey won’t need to talk to her at all.
ey only need to take her photo. FBI co-authored
research suggests that these systems may be least
accurate for African Americans, women, and young
people aged 18 to 30.
5
Chris is 26. She is black.
Unless she initiates a special court proceeding to
expunge her record, she will be enrolled in these
databases for the rest of her life.
6
What happened to Chris doesn’t aect only
activists: Increasingly, law enforcement face
recognition systems also search state driver’s
license and ID photo databases. In this way,
roughly one out of every two American adults
(48%) has had their photo enrolled in a criminal
face recognition network.
7
ey may not know it, but Chris Wilson and
over 117 million American adults are now
part of a virtual, perpetual line-up. What does
this mean for them? What does this mean for
our society? Can police use face recognition
to identify only suspected criminals—or can
they use it to identify anyone they want? Can
police use it to identify people participating in
protests? How accurate is this technology, and
does accuracy vary on the basis of race, gender or
age? Can communities debate and vote on the
use of this technology? Or is it being rolled out
in secret?
FBI and police face recognition systems have
been used to catch violent criminals and fugitives.
eir value to public safety is real and compelling.
But should these systems be used to track Chris
Wilson? Should they be used to track you?
9
A. WHAT IS FACE RECOGNITION
TECHNOLOGY?
Face recognition is the automated process of
comparing two images of faces to determine
whether they represent the same individual.
Before face recognition can identify someone,
an algorithm must rst nd that person’s face
within the photo. is is called face detection.
Once detected, a face is “normalized”—scaled,
rotated, and aligned so that every face that the
algorithm processes is in the same position.
is makes it easier to compare the faces.
Next, the algorithm extracts features from the
face—characteristics that can be numerically
quantied, like eye position or skin texture.
Finally, the algorithm examines pairs of faces
and issues a numerical score reecting the
similarity of their features.
Face recognition is inherently probabilistic: It
does not produce binary “yes” or “no” answers,
but rather identies more likely or less likely
matches.
8
Most police face recognition systems
will output either the top few most similar
photos or all photos above a certain similarity
threshold. Law enforcement agencies call these
photos “candidates” for further investigation.
Some facial features may be better indicators of
similarity than others. Many face recognition
algorithms gure out which features matter most
through training. During training, an algorithm
is given pairs of face images of the same person.
Over time, the algorithm learns to pay more
attention to the features that were the most
reliable signals that the two images contained
the same person.
If a training set skews towards
a certain race, the algorithm
may be better at identifying
members of that group.
e make-up of a training set can inuence the
kinds of photos that an algorithm is most adept
at examining. If a training set is skewed towards
a certain race, the algorithm may be better at
identifying members of that group as compared
to individuals of other races.
9
e mathematical machinery behind a face
recognition algorithm can include millions
of variables that are optimized in the process
of training. is intricacy is what gives an
algorithm the capacity to learn, but it also makes
it very dicult for a human to examine an
algorithm or generalize about its behavior.
B. THE UNIQUE RISKS OF FACE
RECOGNITION
Most law enforcement technology tracks your
technology—your car, your phone, or your
computer. Biometric technology tracks your
body.
10
e dierence is signicant.
Americans change smartphones every two and a
half years, and replace cars every ve to six and a
half years.
11
Fingerprints are proven to be stable
III. BACKGROUND
10
The Perpetual Line-Up
for more than a decade, and are believed to be
stable for life.
12
Separately, many states’ driver’s
license renewal requirements ensure that state
governments consistently have an up-to-date
image of a driver’s face.
13
Face recognition allows
tracking from far away, in
secret, and on large numbers
of people.
Here, we can begin to see how face recognition
creates opportunities for tracking—and risks—
that other biometrics, like ngerprints, do not.
Along with names, faces are the most prominent
identiers in human society—online and
oine. Our faces—not ngerprints—are on our
driver’s licenses, passports, social media pages,
and online dating proles. Except for extreme
weather, holidays, and religious restrictions, it
is generally not considered socially acceptable
to cover one’s face; often, it’s illegal.
14
You only
leave your ngerprints on the things you touch.
When you walk outside, your face is captured by
every smartphone and security camera pointed
your way, whether or not you can see them.
Face recognition isn’t just a dierent biometric;
those dierences allow for a dierent kind of
tracking that can occur from far away, in secret,
and on large numbers of people.
Professor Laura Donohue explains that up until
the 21
st
century, governments used biometric
identication in a discrete, one-time manner to
identify specic individuals. is identication
has usually required that person’s proximity or
cooperation—making the process transparent to
that person. ese identications have typically
occurred in the course of detention or in a secure
government facility. Donohue refers to this
form of biometric identication as Immediate
Biometric Identication, or IBI. A prime example
of IBI would be the practice of ngerprinting
someone during booking for an arrest.
In its most advanced forms, face recognition
allows for a dierent kind of tracking. Donohue
calls it Remote Biometric Identication, or
RBI. In RBI, the government uses biometric
technology to identify multiple people in a
continuous, ongoing manner. It can identify
them from afar, in public spaces. Because of this,
the government does not need to notify those
people or get their consent. Identication can be
done in secret.
is is not business as usual: is is a capability
that is “signicantly dierent from that which the
government has held at any point in U.S. history.”
15
C. HOW DOES LAW
ENFORCEMENT USE FACE
RECOGNITION?
e rst successful fully automated face
recognition algorithm was developed in the
early 1990s.
16
Today, law enforcement agencies
mainly use face recognition for two purposes.
Face verication seeks to conrm someone’s
claimed identity. Face identication seeks to
identify an unknown face. is report focuses on
face identication by state and local police and
the FBI.
Law enforcement performs face identication for a
variety of tasks. Here are four of the most common:
• Stop and Identify. On patrol, a police ocer
encounters someone who either refuses or is
unable to identify herself. e ocer takes
her photo with a smartphone or a tablet,
processes that photo through software
11
installed on that device or on a squad car
computer, and receives a near-instantaneous
response from a face recognition system.
at system may compare that “probe” photo
to a database of mug shots, driver’s license
photos, or face images from unsolved crimes,
also known as an “unsolved photo le.” (As
part of this process, the probe photo may
also be enrolled in a database.) is process
is known as eld identication.
• Arrest and Identify. A person is arrested,
ngerprinted and photographed for a
mug shot. Police enroll that mug shot in
their own face recognition database. Upon
enrollment, the mug shot may be searched
against the existing entries, which may
include mug shots, license photos, and an
unsolved photo le. Police may also submit
the arrest record, including mug shot and
ngerprints, to the FBI for inclusion in its
face recognition database, where a similar
search is run upon enrollment.
• Investigate and Identify. While investigating
a crime, the police obtain a photo or video
still of a suspect from a security camera,
smartphone, or social media post—or they
surreptitiously photograph the suspect. ey
use face recognition to search that image
against a database of mug shots, driver’s
licenses, or an unsolved photo le and obtain
a list of candidates for further investigation,
or, in the case of the unsolved photo le, learn
Figure 2: A California policeman displays a mobile face recognition app. (Photo: Sandy Huaker/e New York Times/Redux)
12
The Perpetual Line-Up
if the individual is wanted for another crime.
Alternately, when police believe that a suspect
is using a pseudonym, they search a mug shot
of that suspect against these same databases.
• Real-time Video Surveillance. e police
are looking for an individual or a small
number of individuals. ey upload images
of those individuals to a “hot list.” A face
recognition program extracts faces from
live video feeds of one or more security
cameras and continuously compares them, in
real-time, to the faces of the people on the
hot list. Every person that walks by those
security cameras is subjected to this process.
When it nds a match, the system may send
an alert to a nearby police ocer. Today,
real-time face recognition is computationally
expensive and is not instantaneous.
17
Searches can also be run on archival video.
Face recognition is also used for driver’s license
de-duplication. In this process, a department of
motor vehicles compares the face of every new
applicant for a license or other identication
document to the existing faces in its database,
agging individuals who may be using a
pseudonym to obtain fraudulent identication.
Suspects are referred to law enforcement.
However, because de-duplication is typically
conducted by DMVs, not law enforcement, this use
of the technology will not be a focus of this report.
D. OUR RESEARCH
anks to the May 2016 Government
Accountability Oce report, the public now
has access to basic information about the FBI’s
face recognition programs and their privacy and
accuracy issues. (Sidebar 1.)
By comparison, the public knows very little
about the use of face recognition by state and
local police, even though many of their systems
are older, used more aggressively, and more likely
to have a greater impact on the average citizen.
No one has combined what we know about
FBI systems with information about state and
local face recognition to paint a comprehensive,
national picture of how face recognition is
changing policing in America, and the impact of
these changes on our rights and freedoms.
is report closes these gaps. It begins with
a threshold question: What uses of face
recognition present greater or fewer risks
to privacy, civil liberties, and civil rights?
After proposing a Risk Framework for law
enforcement face recognition, the report explores
the following questions, each of which is
answered in our Findings:
• Deployment. Who is using face recognition,
how often are they using it, and where
do those deployments fall on the Risk
Framework?
• Fourth Amendment. How do agencies using
face recognition protect our right to be free
from unreasonable searches and seizures?
• Free Speech. How do they ensure that face
recognition does not chill our right to free
speech, assembly, and association?
• Accuracy. How do they ensure that their
systems are accurate?
• Racial Bias. How does law enforcement
face recognition impact racial and ethnic
minorities?
• Transparency and Accountability. Are
agencies using face recognition in a way that
is transparent, accountable to the public, and
subject to internal oversight?
13
13
The Perpetual Line-Up
e FBI hosts one of the largest face recognition
databases in the country, the Next Generation
Identication Interstate Photo System (NGI-
IPS). It is also home to a unit, Facial Analysis,
Comparison, and Evaluation (FACE) Services,
that supports other FBI agents by running
or requesting face recognition searches of the
FBI face recognition database, other federal
databases, and state driver’s license photo and
mug shot databases. (is report will refer to
NGI-IPS as “the FBI face recognition database
(NGI-IPS),” and will refer to FBI FACE
Services as “the FBI face recognition unit
(FACE Services).” e network of databases that
the unit searches will be called “the FBI FACE
Services network”).
e FBI face recognition database (NGI-IPS) is
mostly made up of the mug shots accompanying
criminal ngerprints submitted to the FBI
by state, local, and federal law enforcement
agencies. It contains nearly 25 million state and
federal criminal photos.
19
Police in seven states
can run face recognition searches against the
FBI face recognition database, as can the FBI
face recognition unit.
20
e FBI face recognition unit (FACE Services)
runs face recognition searches against a network
of databases that includes 411.9 million photos.
Over 185 million of these photos are drawn from
12 states that let the FBI search their driver’s
license and other ID photos; another 50 million
are from four additional states that let the FBI
to search both driver’s license photos and mug
shots.
21
While we do not know the total number
of individuals that those photos implicate, there
are close to 64 million licensed drivers in those
16 states.
22
SIDEBAR 1:
FACE RECOGNITION AT THE FBI
e FBI has used face recognition to support FBI and state
and local police investigations since at least 2011.
18
e FBI is expanding the reach of the FACE
Services network, but the details are murky. In
October 2015, the FBI began a pilot program
to search photos against the State Department’s
passport database, but it is unclear if the FBI
is searching the photos of all 125 million U.S.
passport holders, or if it is searching a subset of
that database.
23
In a May 2016 report, the Government
Accountability Oce reported that the FBI
was negotiating with 18 additional states and
the District of Columbia to access their driver’s
license photos. In August, the GAO re-released
the report, deleting all references to the 18 states
and stating that there were “no negotiations
underway.”
24
e FBI now suggests that FBI
agents had only conducted outreach to those
states to explore the possibility of their joining the
FACE Services network.
25
e GAO report found that the FBI had failed
to issue mandatory privacy notices required by
federal law, failed to conduct adequate accuracy
of the FBI face recognition database (NGI-
IPS) and the state databases that the FBI face
recognition unit accessed, and failed to audit the
state searches of the FBI face recognition database
or any of the face recognition unit’s searches.
26
Despite these ndings, the FBI is proposing to
exempt the FBI face recognition database from key
Privacy Act provisions that guarantee Americans
the right to review and correct non-investigatory
information held by law enforcement—and the right
to sue if their privacy rights are violated.
27
15
To answer all of these questions, we submitted
detailed public records requests to over 100
law enforcement agencies across the country.
In total, our requests yielded more than 15,000
pages of responsive documents. Ninety agencies
provided responsive documents—or substantive
responses—of some kind. ese responses
suggested that at least 52 state and local law
enforcement agencies that we surveyed were now
using, or have previously used or obtained, face
recognition technology. (We will refer to these
agencies as “52 agencies.”) Of the 52 agencies,
eight formerly used or acquired face recognition
but have since discontinued those programs.
Conversely, several other responsive agencies
have opened their systems to hundreds of other
agencies.
28
To support our public records research, we
conducted dozens of interviews with law
enforcement agencies, face recognition
companies, and face recognition researchers and
conducted a fty-state legal survey of biometrics
and related surveillance laws, and an in-depth
review of the technical literature on face
recognition. We conrmed our ndings through
two site visits to law enforcement agencies with
advanced face recognition systems. (Our full
research methodology, including a breakdown
of our records requests and a template for those
requests can be found in the Methodology
section.)
After assessing these risks, this report proposes
concrete recommendations for Congress,
state legislatures, federal, state and local law
enforcement agencies, the National Institute
of Standards and Technology, face recognition
companies, and community leaders.
16
The Perpetual Line-Up
In this section, we categorize police uses of face
recognition according to the risks that they
create for privacy, civil liberties, and civil rights.
Some uses of the technology create new and
sensitive risks that may undermine longstanding,
legally recognized rights. Other uses are far less
controversial and are directly comparable to
longstanding police practices. Any regulatory
scheme should account for those dierences.
A. RISK FACTORS
e overall risk level of a particular deployment
of face recognition will depend on a variety of
factors. As this report will explain, it is unclear
how the Supreme Court would interpret the
Fourth Amendment or First Amendment to
apply to law enforcement face recognition;
there are no decisions that directly answer these
questions.
29
In the absence of clear guidance,
we can look at general Fourth Amendment and
First Amendment principles, social norms, and
police practices to identify ve risk factors for
face recognition. When applied, these factors
will suggest dierent regulatory approaches for
dierent uses of the technology.
• Targeted vs. Dragnet Search. Are searches
run on a discrete, targeted basis for
individuals suspected of a crime? Or are they
continuous, generalized searches on groups
of people—or anyone who walks in front of a
surveillance camera?
At its core, the Fourth Amendment was
intended to prevent generalized, suspicionless
searches. e Fourth Amendment was inspired
by the British Crown’s use of general warrants,
also known as Writs of Assistance, that entitled
ocials to search the homes of any colonial
resident.
30
is is why the Amendment requires
that warrants be issued only upon a showing of
probable cause and a particularized description
of who or what will be searched or seized.
31
For its part, the First Amendment does not
protect only our right to free speech. It also
protects our right to peaceably assemble,
to petition the government for a redress
of grievances, and to express ourselves
anonymously.
32
ese rights are not total
and the cases interpreting them are at times
contradictory.
33
But police use of face
recognition to continuously identify anyone on
the street—without individualized suspicion—
could chill our basic freedoms of expression and
association, particularly when face recognition is
used at political protests.
• Targeted vs. Dragnet Database. Are
searches run against a mug shot database,
or an even smaller watchlist composed of
handful of individuals? Or are searches run
against driver’s license photo databases that
include millions of law-abiding Americans?
Government searches against dragnet databases
have been among the most controversial national
security and law enforcement policies of the 21
st
century. Public protest after Edward Snowden’s
leaks of classied documents centered on the
NSA’s collection of most domestic call records in
IV. A RISK FRAMEWORK FOR
LAW ENFORCEMENT FACE RECOGNITION
17
the U.S. Notably, searches of the data collected
were more or less targeted; the call records
database was not.
34
In 2015, Congress voted to
end the program.
35
• Transparent vs. Invisible Searches. Is
the face recognition search conducted in a
manner that is visible to a target? Or is that
search intentionally or inadvertently invisible
to its target?
While they may stem from legitimate law
enforcement necessity, secret searches merit
greater scrutiny than public, transparent search-
es. “It should be obvious that those government
searches that proceed in secret have a greater
need for judicial intervention and approval than
those that do not,” writes Professor Susan Frei-
wald. As she explains, “[i]nvestigative methods
that operate out in the open may be challenged
at the time of the search by those who observe
it.” You can’t challenge a search that you don’t
know about.
36
• Real-time vs. After-the-Fact Searches.
Does a face recognition search aim to
identify or locate someone right now? Or is
it run to investigate a person’s past behavior?
Courts have generally applied greater
scrutiny—and required either a higher level
of individualized suspicion, greater oversight,
or both—to searches that track real-time,
contemporaneous behavior, as opposed to past
conduct. For example, while there is a split in
the federal courts as to whether police need
probable cause to obtain historical geolocation
records, most courts have required warrants for
real-time police tracking.
37
Similarly, while
federal law enforcement ocers obtain warrants
prior to requesting real-time GPS tracking of a
suspect’s phone by a wireless carrier, and prior
to using a “Stingray” (also known as a cell-site
simulator) which eectively conducts a real-time
geolocation search, courts have generally not
extended that standard to requests for historical
geolocation records.
38
• Established vs. Novel Use. Is a face
recognition search generally analogous
to longstanding ngerprinting practices
or modern DNA analysis? Or is it
unprecedented?
In 1892, Sir Francis Galton published
Finger Prints, a seminal treatise arguing that
ngerprints were “an incomparably surer
criterion of identity than any other bodily
feature.” Since then, law enforcement agencies
have adopted ngerprint technology for
everyday use. A comparable shift occurred in the
late 20
th
century around forensic DNA analysis.
Regulation of face recognition should take
note of the existing ways in which biometric
technologies are used by American law
enforcement.
At the same time, we should not put precedent
on a pedestal. As Justice Scalia noted in his
dissent in Maryland v. King, a case that explored
the use of biometrics in modern policing,
“[t]he great expansion in ngerprinting came
before the modern era of Fourth Amendment
jurisprudence, and so [the Supreme Court was]
never asked to decide the legitimacy of the
practice.”
40
A specic use of biometrics may be
old, but that doesn’t mean it’s legal.
B. RISK FRAMEWORK
Applying these criteria to the most common
police uses of face recognition, three categories
of deployments begin to emerge:
• Moderate Risk Deployments are more
targeted than other uses and generally
resemble existing police use of biometrics.
18
The Perpetual Line-Up
• High Risk Deployments involve the
unprecedented use of dragnet biometric
databases of law-abiding Americans.
• Very High Risk Deployments apply
continuous face recognition searches to
video feeds from surveillance footage and
police-worn body cameras, creating profound
problems for privacy and civil liberties.
ese categories are summarized in Figure 3 and
explained below. Note that not every criterion
maps neatly onto a particular deployment.
Figure 3: Risk Framework for Law Enforcement Face Recognition.
DEPLOYMENT LESS RISK MORE RISK
Stop and Identifty
(Mug shot Database)
• Targeted Search
• Targeted Database
• Transparent
• Real-Time
• Novel Use
Arrest and Identify
(Mug shot Database)
• Targeted Search
• Targeted Database
• Established Use
• Invisible
Investigate and Identify
(Mug shot Database)
• Targeted Search
• Targeted Database
• After-the-Fact
• Established Use
• Invisible
Stop and Identify
(License Database)
• Targeted Search
• Transparent
• Dragnet Database
• Real-Time
• Novel Use
Arrest and Identify
(License Database)
• Targeted Search
• Dragnet Database
• Invisible
• Novel Use
Investigate and Identify
(License Database)
• Targeted Search
• After-the-Fact
• Dragnet Database
• Invisible
• Novel Use
Real-Time Video
Surveillance
• Targeted Database
• Dragnet Search
• Invisible
• Real-Time
• Novel Use
Historical Video
Surveillance
• Targeted Database
• After-the-Fact
• Dragnet Search
• Invisible
• Novel Use
MODERATE RISKHIGH RISKVERY HIGH RISK
19
1. MODERATE RISK
DEPLOYMENTS.
e primary characteristic of moderate risk
deployments is the combination of a targeted
search with a relatively targeted database.
When a police ocer uses face recognition to
identify someone during a lawful stop (Stop and
Identify), when someone is enrolled and searched
against a face recognition database after an arrest
(Arrest and Identify), or when police departments
or the FBI use face recognition systems to
identify a specic criminal suspect captured by
a surveillance camera (Investigate and Identify),
they are conducting a targeted search pursuant
to a particularized suspicion—and adhering to a
basic Fourth Amendment standard.
Most face recognition searches
are eectively invisible.
Mug shot databases are not entirely “targeted.”
ey’re not limited to individuals charged with
felonies or other serious crimes, and many of
them include people like Chris Wilson—people
who had charges dismissed or dropped, who
were never charged in the rst place, or who were
found innocent of those charges. In the FBI face
recognition database (NGI-IPS), for example,
over half of all arrest records fail to indicate
a nal disposition.
41
e failure of mug shot
databases to separate the innocent from the guilty—
and their inclusion of people arrested for peaceful,
civil disobedience—are serious problems that must
be addressed. at said, systems that search against
mug shots are unquestionably more targeted than
systems that search against a state driver’s license
and ID photo database.
Two of the three moderate risk deployments—
while invisible to the search subject—mirror
longstanding police practices. e enrollment
and search of mug shots in face recognition
databases (Arrest and Identify) parallels the
decades-old practice of ngerprinting arrestees
during booking. Similarly, using face recognition
to compare the face of a bank robber—captured
by a security camera—to a database of mug shots
(Investigate and Identify) is clearly comparable
to the analysis of latent ngerprints at crime
scenes.
e other lower risk deployment, Stop and
Identify, is eectively novel.
42
It is also
necessarily conducted in close to real-time. On
the other hand, a Stop and Identify search is the
only use of face recognition that is somewhat
transparent. When an ocer stops you and asks
to take your picture, you may not know that he’s
about to use face recognition—but it certainly
raises questions.
43
e vast majority of face
recognition searches are eectively invisible.
44
2. HIGH RISK DEPLOYMENTS.
High risk deployments are quite similar to
moderate risk deployments—except for the
databases that they employ. When police or
the FBI run face recognition searches against
the photos of every driver in a state, they create
a virtual line-up of millions of law-abiding
Americans—and cross a line that American law
enforcement has generally avoided.
Law enforcement ocials emphasize that they
are merely searching driver’s license photos that
people have voluntarily chosen to provide to state
government. “Driving is a privilege,” said Sheri
Gualtieri of the Pinellas County Sheri ’s Oce.
45
20
The Perpetual Line-Up
People in rural states—and states with voter
ID laws—may chafe at the idea that getting a
driver’s license is a choice, not a necessity. Most
people would also be surprised to learn that
by getting a driver’s license, they “volunteer”
their photos to a face recognition network
searched thousands of times a year for criminal
investigations.
at surprise matters: A founding principle
of American privacy law is that government
data systems should notify people about how
their personal information will be used, and
that personal data should not be used outside
of the “stated purposes of the [government
data system] as reasonably understood by the
individual, unless the informed consent of the
individual has been explicitly obtained.”
46
Most critically, however, by using driver’s
license and ID photo repositories as large-
scale, biometric law enforcement databases,
law enforcement enters controversial, if not
uncharted, territory.
Never before has federal law
enforcement built a biometric
network primarily made up of
law-abiding Americans.
Historically, law enforcement biometric
databases have been populated exclusively or
primarily by criminal or forensic samples. By
federal law, the FBI’s national DNA database,
also known as the National DNA Index System,
or “NDIS,” is almost exclusively composed of
DNA proles related to criminal arrests or
forensic investigations.
47
Over time, the FBI’s
ngerprint database has come to include non-
criminal records—including the ngerprints
of immigrants and civil servants. However,
as Figure 4 shows, even when one considers
the addition of non-criminal ngerprint
submissions, the latest gures available suggest
that the ngerprints held by the FBI are still
primarily drawn from arrestees.
e FBI face recognition unit (FACE Services)
shatters this trend. By searching 16 states’
driver’s license databases, American passport
photos, and photos from visa applications,
the FBI has created a network of databases
that is overwhelmingly made up of non-
criminal entries. Never before has federal law
enforcement created a biometric database—or
network of databases—that is primarily made up
of law-abiding Americans. Police departments
should carefully weigh whether they, too, should
cross this threshold.
48
21
Figure 4: Criminal vs. Non-Criminal Makeup of FBI Biometric Databases & Networks.
FBI Face Recognition Database
(NGI-IPS) (2016)
FBI Face Recognition Network
(FACE Services) (2016)
100
%
41
%
59
%
84
%
16
%
80
%
12
%
8
%
FBI DNA Database
(NDIS) (2016)
FBI Fingerprint Database
(formerly IAFIS) (2016)
Criminal or Forensic
Non-Criminal
Unknown
Sources: FBI, GAO.
49
22
The Perpetual Line-Up
3. VERY HIGH RISK
DEPLOYMENTS.
Real-time face recognition marks a radical
change in American policing—and American
conceptions of freedom. With the unfortunate
exception of inner-city black communities—
where suspicionless police stops are all too
common
50
—most Americans have always
been able to walk down the street knowing that
police ocers will not stop them and demand
identication. Real-time, continuous video
surveillance changes that. And it does so by
making those identications secret, remote, and
potentially pervasive. What’s more, as this report
explains, real-time identications may also be
signicantly less accurate than identications in
more controlled settings (i.e. Stop and Identify,
Arrest and Identify).
51
In a city equipped with real-time face
recognition, every person who walks by a street
surveillance camera—or a police-worn body
camera—may have her face searched against
a watchlist. Right now, technology likely
limits those watchlists to a small number of
individuals. Future technology will not have
such limits, allowing real-time searches to be run
against larger databases of mug shots or even
driver’s license photos.
52
ere is no current analog—in technology or
in biometrics—for the kind of surveillance
that pervasive, video-based face recognition
can provide. Most police geolocation tracking
technology tracks a single device, requesting
the records for a particular cell phone from a
wireless company, or installing a GPS tracking
device on a particular car. Exceptions to that
trend—like the use of cell-tower “data dumps”
and cell-site simulators—generally require either
a particularized request to a wireless carrier or
the purchase of a special, purpose-built device
(i.e., a Stingray). A major city like Chicago may
own a handful of Stingrays. It reportedly has
access to 10,000 surveillance cameras.
53
If cities
like Chicago equip their full camera networks
with face recognition, they will be able to track
someone’s movements retroactively or in real-
time, in secret, and by using technology that
is not covered by the warrant requirements of
existing state geolocation privacy laws.
54
23
We studied the (1) Deployment of face
recognition technology—namely how many
agencies use face recognition, how often they
use it, and the risk level of those uses. We also
studied the measures that agencies and other
stakeholders apply to protect (2) our Fourth
Amendment rights and (3) our right to Free
Speech, and evaluated the steps they took to
protect against (4) Accuracy problems and the
potential for (5) Racial Bias in error rates and
in use more generally. Finally, we studied the (6)
Transparency & Accountability provisions in
place at agencies using the technology.
is section outlines our top-level ndings in
each of these areas. In our Face Recognition
Scorecard, and in the City and State
Backgrounders we evaluate how each of 25
specic agencies, plus the FBI performs in these
same elds.
55
e criteria for the scores are
described in their corresponding subsections. Two
of the subsections, Deployment and Transparency
& Accountability, are measured with two separate
scores, whereas one subsection, Racial Bias, is not
scored at all. (Our full scorecard methodology can
be found in the report on page 93.)
Before proceeding further, a disclaimer is in
order. Our ndings are based on 15,000 pages
of documents provided in response to over 100
records requests. Many of those records were
partial, redacted, or otherwise incomplete. We
have made extensive eorts to give state and
local police departments the ability to review
and correct our conclusions regarding their
face recognition systems, but it is inevitable
that errors and misunderstandings will occur.
We invite agencies to contact the authors
with corrections and clarications—and
improvements to their systems—so that this
report may be updated accordingly.
A. DEPLOYMENT
Neil Stammer was a fugitive wanted for child
abuse and kidnapping who had evaded capture
for 14 years after failing to show up for his
arraignment. en, in 2014, a State Department
ocial with the Diplomatic Security Service
ran the FBI’s wanted posters through a database
designed to detect passport fraud—and got a hit
for Kevin Hodges, an American living in Nepal.
It was Stammer, who’d been living in Nepal for
years under a pseudonym. He was arrested and
returned to the United States to face charges.
56
e year before Stammer was caught, on the
other side of the world, the Los Angeles Police
Department announced the installation of
16 new surveillance cameras in “undisclosed
locations” across the San Fernando Valley. e
cameras were mobile, wireless, and programmed
to support face recognition “at distances of up to
600 feet.”
57
LA Weekly reported that they fed into
the LAPD’s Real-time Analysis and Critical
Response Center, which would scan the faces in
the feed against “hot lists” of wanted criminals or
“documented” gang members.
58
It appears that
every person who walks by those cameras has
her face searched in this way.
V. FINDINGS & SCORECARD
24
The Perpetual Line-Up
FACE RECOGNITION SCORECARD
Positive Caution Negative
25
What agencies are using face recognition for law
enforcement, how often are they using it, and
how risky are those deployments?
When proponents of face recognition answer
these questions, they often cite cases like Neil
Stammer’s: A felon, long-wanted for serious
crimes, is nally brought to justice through
the last-resort use of face recognition by a
sophisticated federal law enforcement agency.
59
e LAPD’s system suggests a more sobering
reality: Police and the FBI use face recognition
for routine, day-to-day law enforcement. And
state and local police, not the FBI, are leading
the way towards the most advanced—and
highest risk—deployments.
1. How many law enforcement agencies use
face recognition?
We estimate that more than one in four of
all American state and local law enforcement
agencies can run face recognition searches of
their own databases, run those searches on
another agency’s face recognition system, or have
the option to access such a system.
60
Some of the longest-running and largest systems
are found at the state and local level. e Pinellas
County Sheri ’s Oce in Florida, for example,
began implementing its current system in 2001.
61
Over 5,300 ocials from 242 federal, state, and
local agencies have access to the system.
62
In
Pennsylvania, ocials from over 500 agencies
already use the state’s face recognition system,
which is open to all 1,020 law enforcement
agencies in the state.
63
Many federal agencies access state face
recognition systems. While the GAO reports
that the FBI face recognition unit (FACE
Services) searches 16 state driver’s license
databases,
64
this is not a complete picture of the
FBI’s reach into state systems: We found that,
after undergoing training, FBI agents in Florida
eld oces have direct access to the Pinellas
County Sheri ’s Oce system, which can run
searches against all of Florida driver’s license
photos. Notably, the GAO does not identify
Florida as forming part of the FACE Services
network.
65
It is possible that other eld oces
can access other state systems, such as those in
Pennsylvania and Maryland.
66
e Department of Defense, the Drug
Enforcement Administration, Immigration and
Customs Enforcement, the Internal Revenue
Service, the Social Security Administration, the
U.S. Air Force Oce of Special Investigations,
and the U.S. Marshals Service have all had access
to one or more state or local face recognition
systems.
67
2. How often do law enforcement agencies
use face recognition?
Face recognition searches are routine at the
federal and state level. FBI face recognition
searches of state driver’s license photos are
almost six times more common than federal
court-ordered wiretaps.
68
From August 2011
to December 2015, the FBI face recognition
unit (FACE Services) ran close to 214,920
face recognition searches, including 118,490
searches of its own database and 36,420 searches
against the 16 state driver’s license and mug
shot databases. e remainder was run against
the Department of Defense database and the
Department of State’s visa and passport photo
databases.
69
In its rst eight months of operation, Ohio’s
system was used 6,618 times by 504 agencies,
though its usage rate has since gone down—for
the rst four months of 2016, the system was
searched 1,429 times by 104 dierent agencies.
70
26
The Perpetual Line-Up
San Diego agencies run an average of around
560 searches of the San Diego Association of
Government’s system each month.
71
Pinellas
County’s system may be the most widely
used; its users conduct around 8,000 searches
per month. is appears to be much more
frequent than the searches run by the FBI face
recognition unit—almost twice as often, on
average.
73
We have only a partial sense of how eective
these searches are. ere are no public statistics
on the success rate of state face recognition
systems. We do know the number of FBI
searches that yielded likely candidates—
although we do not know how many actual
identications resulted from those potential
matches. e statistics are nonetheless striking:
Of the FBI’s 36,420 searches of state license
photo and mug shot databases, only 210
(0.6%) yielded likely candidates for further
investigations. Overall, 8,590 (4%) of the FBI’s
214,920 searches yielded likely matches.
74
3. How risky are those deployments?
We found that a large number of police
departments are engaging in high risk
deployments, and that several of the agencies are
actively exploring real-time video surveillance.
a) Moderate Risk Deployments
Of the 52 agencies we surveyed which were
now using or had previously used or obtained
face recognition technology, we identied 29
that are deploying face recognition under a
Moderate Risk model—Stop and Identify,
Arrest and Identify, and/or Investigate and
Identify o of mug shot databases. Most of the
agencies use their systems in a variety of ways.
Only one current system, used by the San Diego
Association of Governments (SANDAG), is
designed to be used only for Stop and Identify
searches.
75
None of the agencies indicated that its mug
shot database was limited to individuals arrested
for felonies or other serious crimes. Only one
agency, the Michigan State Police, deleted mug
shots of individuals who are not charged or
found innocent.
76
e norm, rather, is reected
in an agency like the Pinellas County Sheri ’s
Oce. Its mug shot database is not scrubbed to
eliminate cases that did not result in conviction.
To be removed from the database, individuals
need to obtain an expungement order—a process
that can take months to be resolved.
77
b) High Risk Deployments
High risk deployments—whether Stop and
Identify, Arrest and Identify, or Investigate
and Identify—are typied by their access to
state driver’s license and ID photo databases.
Our requests revealed 19 state or local law
enforcement agencies in eight states allow
face recognition searches of these databases.
Combining that with recent information from
the GAO, and earlier reporting that we veried
against the GAO report or through our own
research, we identied 26 states that enroll their
residents in a virtual line-up.
78
In 2014, there were 119,409,269 drivers in these
states, of whom 117,673,662 were adults aged
18 or older and 1,736,269 were minors aged 17
or younger.
79
e U.S. Census estimated that in
2014, there were 245,273,438 American adults
in the country.
80
is means that, at a minimum,
roughly 1 in 2 American adults (48%) have
had their photos enrolled in a criminal face
recognition network.
27
e gure is likely larger than that. In 2013, the
Washington Post and the Cincinnati Enquirer
conducted similar surveys that agged four other
states—Indiana, Massachusetts, Mississippi
and South Dakota—that allowed access but that
we were not able to verify.
81
If all four of those
states continue to grant access, the total number
of licensed drivers in face recognition networks
would increase to 131,211,203, of whom
129,280,396 were adults.
82
at comes out to
53% of the adult population.
83
c) Very High Risk Deployments
In May 2016, one of the world’s leading face
recognition companies reportedly entered into
an agreement with the city government of
Moscow, Russia. e company, called N-Tech.
Lab, would test their software on footage from
Moscow’s CCTV cameras. “People who pass by
the cameras are veried against the connected
database of criminals or missing persons,” the
company’s founder said. “If the system signals
a high level of likeness, a warning is sent to a
police ocer near the location.” Following the
trial, the company will reportedly install its
software on Moscow’s CCTV system. e city
has over 100,000 surveillance cameras.
86
Is real-time video surveillance like that seen in
Moscow coming to major American cities? e
answer to this question is likely “yes.”
In the U.S., no police department other than
the LAPD openly claims to use real-time
face recognition. But our review of contract
documents and other reports suggests that at least
four other major police departments have bought
or expressed plans to buy real-time systems.
• In 2012, the West Virginia Intelligence
Fusion Center purchased a system with
the ability to “automatically monitor video
surveillance footage and other video for
instances of persons of interest.”
87
• In a 2012 grant application, the Chicago
Police Department requested funds for high-
end video processing servers “congured
to process video analytics and facial
recognition… to allow for real-time analysis
of simultaneous high quality video streams.”
88
• In 2012, South Sound 911 in Washington
state wrote in its Request for Proposals for
face recognition capabilities: “e system
should have the ability to do facial recognition
searches against live-feed video.”
89
However,
the nal manual for the face recognition
system, which was adopted by the Seattle
Police Department, states that it “may not be
used to connect with ‘live’ camera systems.”
90
• e Dallas Area Rapid Transit police
announced plans to deploy real-time face
recognition software throughout its system
sometime in 2016.
91
is means that ve major American police
departments either claim to use real-time video
surveillance, have bought the necessary hardware
and software, or have expressed a written interest
in buying it.
e supply exists to meet this demand. Almost
all major face recognition companies advertise
real-time face recognition systems. Specically:
• NEC, the top performer in NIST
accuracy tests,
92
advertises that “[f]ace
recognition can do far more than is generally
understood,” and oers an “application for
real-time video surveillance” that can
“[d]etect[] subjects in a crowd in real time.”
93
28
The Perpetual Line-Up
Figure 5: Drivers and Adults in Law Enforcement Face Recognition Networks (2014)
States and agencies reported to allow or run searches of license and ID photos are in italics
84
Note: is is not an exhaustive accounting of law enforcement access to driver’s license photo databases. Other states may allow this access that were not identied in our research.
Sources: GAO, FOIA documents, U.S. Dep’t of Transportation, Federal Highway Administration, Washington Post, Cincinnati Enquirer, Police Executive Research Forum
STATE LICENSE
PHOTO DATABASE
WHAT LAW ENFORCEMENT AGENCIES CAN
RUN OR REQUEST FACE RECOGNITION SEARCHES?
NUMBER OF
DRIVERS (2014)
29
• Cognitec advertises its “FaceVACS—
VideoScan” solution to “instantly detect,
track, recognize and analyze people in live
video streams or video footage.”
94
• 3M Cogent recently introduced a new “3M
Live Face Identication System” that “uses
live video to match identities in real time . . .
e system automatically recognizes multiple
faces . . . to identify individual people from
dynamic, uncontrolled environments.”
95
• Safran Identity & Security oers Morpho
Argus, a “real-time video screening system,
processing faces captured within live or pre-
recorded video streams.”
96
• Dynamic Imaging has advertised a system
add-on that would “support the ability to
perform facial recognition searches against
live-feed video.”
97
• DataWorks Plus claims to be able to
“[r]apidly detect faces in live video
surveillance monitoring for face
recognition.”
98
e proliferation of police body-worn cameras
presents another opportunity for real-time face
recognition. Rick Smith, the CEO of Taser, the
leading manufacturer of body cameras, recently
told Bloomberg Businessweek that he expects
real-time face recognition o of live streams
from body cameras to eventually become a
reality.
99
In a recent interview with Vocativ,
the director of the West Virginia Intelligence
Fusion Center, omas Kirk, had a similar
vision: “Everyone refers to the Minority Report…
about how they use facial recognition and iris
recognition. I actually think that that is the way
of the future.”
100
We anticipate that real-time
face recognition systems will
become commonplace.
Researchers and industry experts we interviewed
agreed that real-time face recognition is
becoming technologically feasible, but that
computational limitations, video quality, and
poor camera angles constrain its eectiveness
and sharply limit its accuracy. NIST is currently
running the rst ever test for face recognition in
video, which should shed light on the accuracy and
performance of these algorithms in real-time.
101
Real-time video surveillance appears to be a simple
question of supply and demand. As the technology
improves, we anticipate that real-time face
recognition systems will become commonplace.
SIDEBAR 2:
SCORING AGENCY DEPLOYMENT
We developed two scores to measure the risk level of an agency’s
deployment. e rst focuses on the main dierentiator between
moderate and high risk systems—the people enrolled in the system’s
face recognition database. e second focuses on how the agency has
addressed real-time or historical video surveillance.
PEOPLE IN THE DATABASE.
Who is enrolled in the face recognition database
or network of databases available to the law
enforcement agency?
Mug shots of individuals arrested,
with enrollment limited based on the
underlying oense, and/or with mug
shots armatively “scrubbed” by police
to eliminate no-charge arrests or not-
guilty verdicts.
Mug shots of individuals arrested, with
no limits or rules to limit which mug
shots are enrolled, or where mug shots
are removed only after the individual
applies for, and is granted, expungement.
Driver’s license photos in addition to
mug shots of individuals arrested.
REAL-TIME VIDEO SURVEILLANCE.
How has the agency addressed the risks of real-time
or historical video surveillance?
Written policy (1) prohibiting the
use of face recognition for real-time
video or historical video surveillance,
or (2) that restricts its use only to life
threatening public emergencies and
requires a time-limited warrant.
No written policy addressing real-time
or historical video surveillance, but
agency has armatively stated that it
does not use face recognition in this
manner.
Agency has deployed, purchased,
or indicated a written interest in
purchasing face recognition for real-
time or historical video surveillance but
has not developed a written policy or
armatively disclaimed these practices.
31
B. FOURTH AMENDMENT
e two district courthouses serving
Cheltenham Township, Pennsylvania, adjudicate
landlord-tenant disputes and municipal
ordinance violations, and also hold preliminary
hearings and arraignments on more serious
criminal charges.
102
Several years ago, the
Cheltenham Township Police Department
stationed ocers outside a courthouse parking
lot to “perform counter-surveillance”—taking
photos of people attending the court hearing
of an alleged gang member. ese photos were
then run through Pennsylvania’s face recognition
system, which searches state mug shots and,
beginning in 2012, all 34 million driver’s
license photos.
103
We do not know if the photos
were taken of suspected criminals—or if they
were just people who happened to be in the
courthouse parking lot.
is may seem unremarkable: Surreptitious
police photography is an established policing
technique. While the Fourth Amendment
Figure 6: Excerpts from an NEC brochure for the NeoFace Watch real-time face recognition system.
32
The Perpetual Line-Up
protects us against “unreasonable searches and
seizures,” it is unclear whether face recognition
constitutes a “search.” (See Sidebar 3.)
Protections from “unreasonable searches and
seizures” can originate in any of the three
branches of government: the judiciary, the
legislature, or the executive, which includes
law enforcement. Instead of allowing those
protections to grow old and out of date, however,
legislatures across the country are passing
dozens of laws restricting the use of 21
st
century
tracking technology to monitor public conduct.
When legislators have hesitated, state and
federal courts have stepped in and interpreted
the Fourth Amendment to require warrants and
other protections.
Unfortunately, courts and legislatures by and
large have not applied these protections to
face recognition technology. In the absence of
guidance from legislatures and courts, police
departments have created systems that often fall
short of the protections oered against other
tracking technology.
Figure 7: Will the Fourth Amendment limit how face recognition is used? Or will the police be able to use it to identify anyone?
(Photo: Mike Voss/Alamy)
SIDEBAR 3:
FACE RECOGNITION AND THE FOURTH AMENDMENT
Before 1967, the Supreme Court generally adhered to a property-based
view of the Fourth Amendment. Judges’ rulings on whether or not a Fourth
Amendment “search” occurred largely turned on the existence of trespass.
104
In 1967, however, the Court declared in Katz v.
U.S. that “the Fourth Amendment protects people,
not places.”
105
In a concurrence, Justice Harlan set
forward a test, additional to trespass, to determine
whether or not a Fourth Amendment “search” had
occurred: Has the government infringed on an
expectation of privacy that “society is prepared to
recognize as ‘reasonable’”?
106
is became known as
the “reasonable expectation of privacy” test.
e Supreme Court has never formally recognized
a reasonable expectation of privacy in public
conduct. In the 1983 case of U.S. v. Knotts, the
Supreme Court found that the use of technology
to track a person’s public movements—movements
otherwise visible to the naked eye—did not infringe
a reasonable expectation of privacy.
107
In U.S. v.
Jones (2012), the Court reiterated that it had not
“deviated from the understanding that mere visual
observation does not constitute a search.”
108
Nevertheless, in Jones, a “shadow majority” of ve
justices expressed a willingness to reevaluate the
contours of the reasonable expectation of privacy
test to encompass some forms of geolocation
tracking of public movements.
109
In later cases, the
Supreme Court highlighted the transformational
nature of 21
st
century surveillance technology—
and rejected simplistic comparisons of modern
technology to older policing practices. In Riley
v. California (2014), for example, the Court
ridiculed the government’s contention that a
search of an arrestee’s smartphone was “materially
indistinguishable” from a search of a person’s
pockets upon arrest. “at is like saying a ride on
horseback is materially indistinguishable from a
ight to the moon,” Justice Roberts wrote.
110
At publication, no cases in any state or federal
court—let alone the Supreme Court—have
addressed whether any form of law enforcement
face recognition constitutes a Fourth Amendment
search. It is unclear whether the Court would treat
face recognition as being tantamount to “mere visual
observation”—or if the Court would analogize it to
space travel.
34
The Perpetual Line-Up
1. Courts have limited geolocation tracking—
but not face recognition.
Except for forensic analysis of latent ngerprints
and DNA, law enforcement collection of
biometric information has typically required a
physical search or handling of a suspect—e.g.,
reaching into their mouth for a buccal swab, or
rolling their ngers on an inkpad. e physical
nature of these searches or seizures may seem
like a small detail, but it has major consequences
for the Fourth Amendment: Namely, it means
that judges have felt comfortable regulating that
conduct under the Fourth Amendment.
111
Face recognition changes the equation by
allowing tracking and identication outside of a
traditional Fourth Amendment search or seizure.
e Pinellas County Sheri ’s Oce’s use policy
for mobile biometric identication (Figure 8
below) illustrates this powerfully.
3. Individuals will not be physically detained for the purpose of taking a biometric sample for identication. Deputies
should ask for consent; however this does not preclude a deputy taking the photograph of a person in a public
place provided the deputy has not hindered the movement of the person
a. Physical force shall not be used for the purpose of taking a photograph or ngerprint.
b. An individual in public shall not be stopped or told to pose for a photograph when it is not being done for a law
enforcement investigation, i.e., a person in a motor vehicle shall not be required to roll down tinted windows or
uncover their face just for the purpose of taking their photograph.
4. All biometric and search activity are logged and subject to audit.
5. Deputies are encouraged to use biometric identication whenever practical.
Figure 8: Pinellas County Sheri's Oce, Standard Operating Procedure: Mobile Biometric Usage
112
PCSO bars ocers from physically detaining
individuals, and stresses that the absence of
consent should not preclude ocers from
taking a photograph in a public place. Rather,
if someone is in public, ocers are encouraged
to photograph that person and use biometric
identication “whenever practical.”
113
In this respect, face recognition is not alone—
geolocation tracking via cell-site location
information, automated license plate readers
(ALPRs), and drones also allow tracking through
non-invasive observation. us, as Sidebar 3
suggests, all of these tracking technologies would
seem to fall into a constitutional grey area.
114
And yet a growing number of state supreme
courts and lower federal courts are interpreting
the Fourth Amendment to limit public
surveillance. is is clearest with geolocation
tracking. Federal district courts in California
and New York have found that individuals do
have a reasonable expectation of privacy in the
extended records of their movements revealed
by cell-site location information—and that
the Fourth Amendment requires police to get
35
a warrant to obtain this information.
115
e
highest courts of Massachusetts and New
Jersey have done the same, although each state
reached this conclusion by interpreting their
state constitutions, rather than the Fourth
Amendment.
116
In these cases, the courts
recognized that dragnet-style surveillance raises
serious and novel privacy concerns—and that those
concerns are not extinguished by the fact that the
behavior tracked occurs in public.
117
To date, however, not a single state or federal
court has considered the question of whether a
face recognition search constitutes a search for
the purposes of the Fourth Amendment, or an
analogous provision in a state constitution. As
a result, the Fourth Amendment implications
of face recognition technology remain an open
question.
2. Legislatures have not placed meaningful
limits on law enforcement face recognition.
e Fourth Amendment acts as a oor, not a
ceiling, for the protections the government can
extend to its citizens against a particular police
practice. is means that legislatures are free to
create more privacy protections and safeguards
than the minimum that courts believe the
Fourth Amendment requires.
Legislators across the country have eagerly
passed laws expanding the privacy rights of
citizens against a range of 21
st
century public
tracking technology. A total of 17 states
have passed laws regulating law enforcement
geolocation tracking, and 13 states have passed
laws regulating law enforcement’s use of drones;
these laws generally require that police obtain
warrants, supported by probable cause, before
engaging in tracking.
118
Another 9 states have
passed laws regulating police use of automated
license plate readers (ALPRs).
119
Although
most of these laws do not generally require
warrants, they do generally limit when ALPRs
can be deployed, how the data they collect can
be used and shared, and how long that data can
be kept.
Not a single state has passed a law that places
comprehensive limits on law enforcement use
of face recognition technology. Five states have
passed laws that limit some discrete aspect of
police face recognition use.
• Police Body Worn Camera Footage. In
2015, Oregon passed a law barring face
recognition searches of recordings from
police body-worn cameras, but leaving open
the possibility that face recognition may be
used on live videos rather than recordings.
120
Recently, New Hampshire passed a similar
law, which will take eect in 2017.
121
(Below
the level of state law, the city of Cincinnati
adopted a similar regulation, and six local
police departments have adopted use policies
roughly to this eect.)
122
• Police Drone Footage. Two states, Maine
and Vermont, have passed laws restricting
the use of face recognition on footage
Number of states that
regulate police use of…
13
Drones
9
Automated license
plate readers
17
Geolocation
tracking
5
Face
recognition
36
The Perpetual Line-Up
collected by police drones. e Vermont law
states that face recognition shall not be used
on any data that a drone collects “on any
person, home, or area other than the target of
the surveillance.”
123
e Maine law is more
ambiguous, requiring state ocials to issue
rules for drones that will restrict the use of
face recognition.
124
• Destruction of Records. Michigan law
requires the destruction of biometric
data, including the ngerprint and face
recognition data from people who are
arrested but never charged or who are
found innocent.
125
e law provides little
else by way of protection, however. Instead,
it expressly authorizes the collection of
biometric data for almost all crimes, and
expressly allows non-criminal biometric
information—for example, face recognition
data derived from a driver’s license photo—
to be used for criminal purposes.
126
Given their limited scope, none of these laws
provide the range of protections aorded by
most state laws governing geolocation tracking,
drones, or automated license plate readers.
Apart from regulating police face recognition
systems, seven states directly or indirectly curb
law enforcement access to state department of
motor vehicle face recognition systems, which
are typically designed to detect identity fraud.
Maine, Missouri, New Hampshire and Vermont
have blanket bans on their DMVs using
biometric technology or collecting biometric
data.
127
Washington stipulates that the DMV
can use biometric technology only to verify the
identity of a license or ID card holder.
128
Both
Washington and Oregon prohibit disclosure of
biometric data to law enforcement, although
Washington allows disclosure for identity theft
crimes.
129
Hawaii’s regulations do not expressly
address face recognition, but nonetheless block
law enforcement access to license photos outside
of investigations into identity theft.
130
e few, discrete protections that these laws do
provide may be easily evaded. Vermont law, for
example, expressly prohibits its Department
of Motor Vehicles from implementing
“any procedures or processes for identifying
applicants for licenses, learner permits, or non-
driver identication cards that involve the use
of biometric identiers.”
131
Somehow, however,
Vermont has interpreted this provision to
allow the FBI to request—and obtain—face
recognition searches of 1.8 million Vermont
driver’s license and ID photos.
132
Given that
they do not directly constrain law enforcement,
other states’ DMV provisions could be read in a
similarly narrow manner.
3. Most police departments place few
constraints on face recognition.
In September 2015, the Department of
Justice announced a new policy for federal
law enforcement’s use of cell-site simulators.
Up until that point, the Department had
obtained what it viewed as “appropriate legal
authorization” before using the devices.
133
e
authorization was less than a warrant; rather, the
Department had merely certied to a judge that
the information being obtained was “relevant
to an ongoing criminal investigation.”
134
Now,
however, the Department announced that, not
as a matter of law, but “as a matter of policy,”
federal law enforcement would seek a warrant
before using a cell-site simulator.
135
e Department of Justice’s announcement
illustrates an important and often overlooked
principle: Law enforcement agencies are free
to voluntarily adopt restrictions on tracking
technology that go above and beyond their view
of what current statutes or case law requires.
37
While some agencies have exercised that
authority, a surprising number of police
departments appear to have not taken basic steps
to limit use of face recognition. We can evaluate
these agencies on three simple metrics:
• Have they adopted a use policy telling
ocers when it is appropriate to use face
recognition, and how they should and should
not use it?
• What degree of individual suspicion do they
require prior to running a search?
• Do they limit the use of face recognition to
certain serious oenses?
a) A surprising number of agencies have not
adopted use policies.
Of 52 agencies, at least 24 either did not provide
a face recognition use policy in response to our
document requests, or were not clearly covered
by another agency’s use policy.
136
At least ve
of those agencies—the Daytona Beach Police
Department, the Jacksonville Sheri ’s Oce,
the Nebraska State Patrol, the Kansas City
Police Department (former program), and the
Iowa Department of Public Safety—expressly
acknowledged that they did not have a use policy
(or, in the case of Iowa, a “nalized” use policy)
for law enforcement face recognition.
137
b) A minority of agencies clearly require
individualized suspicion prior to search.
Even though several agencies provided contract
documents for real-time face recognition
systems, not a single agency provided documents
suggesting that they require a warrant—or
judicial approval of any kind—prior to any face
recognition search.
e agencies do, however, impose dierent kinds
of legal restrictions that do not require judicial
approval. Some agencies require ocers to have
an individualized suspicion that the individual
whose photo is being submitted for a search be
involved in a crime, but they vary in the degree
of suspicion required. e agencies may require
that ocers have probable cause to believe that
the individual search for was involved in a crime,
or the agencies may merely require that an ocer
have reasonable suspicion to that eect.
Other agencies do not require any degree
of individualized suspicion and instead only
stipulate that face recognition searches must
be conducted for criminal justice or law
enforcement purposes. In these jurisdictions,
anyone’s face can be searched in their face
recognition database, so long as this is done in
furtherance of a law enforcement mission.
Overall, of the 52 agencies, plus the FBI face
recognition unit (FACE Services), we were able
to determine the legal standard that applied to
face recognition for only 24 of them, plus the
FBI. Of those agencies, three required probable
cause, and 10 required reasonable suspicion.
e remainder either required a criminal justice
purpose or provided no documentation to
suggest a legal standard of any kind. Putting it
dierently, only 13 of 52 agencies (25%) clearly
required any degree of individualized suspicion
(e.g. reasonable suspicion or probable cause)
prior to a face recognition search.
Perversely, as Figure 9 shows, the agencies
engaging in higher risk deployments appear less
likely to require individualized suspicion. Of
the 29 agencies that have used face recognition
under a Moderate Risk deployment model—
either Stop and Identify or Arrest and Identify
using a mug shot database—10 of them (34.5%)
required some form of individualized suspicion.
Meanwhile, of the 24 agencies (including the
FBI) that have used a High Risk deployment—
38
The Perpetual Line-Up
Figure 9: Legal Standards for Face Recognition Search, by Jurisdiction
Agencies reporting discontinued face recognition programs are in italics.
LEGAL STANDARD ALL AGENCIES
MODERATE RISK
DEPLOYMENTS
HIGH RISK
DEPLOYMENTS
PROBABLE
CAUSE
REASONABLE
SUSPICION
CRIMINAL
JUSTICE
OR LAW
ENFORCEMENT
PURPOSE
UNKNOWN
39
Stop and Identify or Arrest and Identify using
a driver’s license database—only three (12.5%)
require individualized suspicion.
Agencies engaging in higher
risk deployments appear less
likely to require individualized
suspicion for a search.
e absence of an individualized suspicion
requirement means that face recognition may
be used on eectively anyone—e.g., a pedestrian
anywhere near a crime—so long as some
criminal justice purpose can be cited for the
search. At least three agencies—including the
FBI face recognition unit (FACE Services)—
expressly allow face recognition searches to
identify witnesses to a crime, not just criminal
suspects.
139
c) Only one agency limits face recognition use
to certain crimes.
When Congress passed the Wiretap Act in
1968, it did not allow wiretaps of oral and phone
communications for all criminal investigations.
Rather, it restricted federal wiretaps of those
communications to investigations of certain
serious federal oenses. Congress gave even
narrower authority to state law enforcement,
allowing wiretaps only for certain felonies.
140
ere are echoes of this trend in modern law
enforcement biometrics. Jurisdictions may search
the FBI face recognition database (NGI-IPS)
for investigations of any crime—regardless of the
nature or the severity of the oense.
141
But in
order to be enrolled in that database’s “unsolved
photo le”—a photo le of unidentied
individuals that is compared to every new photo
enrolled in the database—a photograph must
pertain to an investigation of a felony oense
for criminal homicide, forcible rape, robbery, or
aggravated assault.
142
Likewise, in Maryland v. King, the Supreme
Court upheld a Maryland law requiring the
collection of DNA from all individuals charged
with violent crimes, burglary, or attempted
burglary, and the search of their DNA against
the federal DNA database, which includes
forensic DNA samples from unsolved crimes.
143
In upholding that program and dierentiating
it from a generalized search, however, the
Court cited the “fundamental” distinction that
Maryland’s DNA searches were limited to
individuals arrested, detained, and charged with
a serious criminal oense.
144
None of the 52 responsive agencies clearly
restricted face recognition use to more serious
crimes. Only one, the Nebraska State Patrol,
limited its use to a certain kind of oense—
identity theft.
145
40
The Perpetual Line-Up
Our score for Fourth Amendment protections turns on the level of
individualized suspicion required prior to running a face recognition
search. Where a jurisdiction relies on a driver’s license rather than a mug
shot database, however, the score takes into account proportionality (i.e.,
Does the jurisdiction restrict the use of dragnet-style driver’s license photo
databases to the investigation of serious oenses or identity crimes?). In
other words, our score uses a bifurcated standard. If the agency uses face
recognition on databases that include only mug shots, the rst standard is
used. If the agency uses face recognition on databases that include driver’s
license photos, the second standard is used.
TARGETED DATABASE–
MUG SHOTS ONLY
Reasonable suspicion of the subject
to be searched, and at least one of the
following: (1) Searches are limited to
suspects and victims of crimes; and (2)
Investigate and Identify searches are
limited to felonies only.
Reasonable suspicion of the subject to be
searched but the standard has exceptions
or allows for searches for bystanders or
witnesses as well.
No legal standard stated, or a statement
that face recognition may be used for
any “law enforcement” or “criminal
justice” purpose.
DRAGNET DATABASE–
LICENSE AND ID PHOTOS
(1) Searches are limited to
investigations of serious oenses
and require a warrant or court order
supported by probable cause; or
(2) searches are limited to identity-
related crimes.
Probable cause searches are limited to
investigations of serious oenses (for
non-identity-related crimes).
Anything less than probable cause (for
non-identity crimes).
SIDEBAR 4:
SCORING FOURTH AMENDMENT PROTECTIONS
41
Figure 10: Police videotape a 2010 Los Angeles protest. (Photo: Rogan Ferguson)
C. FREE SPEECH
J. Edgar Hoover kept track of his opponents.
Hoover’s FBI conducted surveillance on Martin
Luther King, Jr., Fannie Lou Hamer, Cesar
Chavez, Marcus Garvey, and many other civil
rights leaders.
146
Public protests were seen as
a threat. FBI agents, disguised as freelance
photographers, were sent to photograph them.
According to a candid memoir from an FBI
undercover photographer, Richard Coman:
Following Martin Luther K.’s ‘I Have
a Dream Speech’ it was discovered that
some of our ‘Most Wanted’ and several
subjects of our Domestic Security
investigations liked to participate or
show up at the various demonstrations.
Many of them mostly attracted by the
anonymity, opportunity of free drugs and
easy available sex. On some of the really
large demonstrations I would recruit a
dozen or so fellow Agents, instruct them
how to not look like FBI Agents and
how to mingle among the ‘Hippies’ and
other protest types and see and report
what was going on.
147
Coman took special interest in surreptitious
photographs of interracial couples and nude or
partially nude female protesters, which he shared
with other law enforcement ocials.
148
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Today’s FBI is a dierent place. In training,
every FBI agent learns about the agency’s
surveillance of Dr. King.
149
But the specter of
political surveillance survives. In a 2012 Senate
hearing, Senator Al Franken, then Chairman
of the Senate Subcommittee on Privacy,
Technology and the Law, confronted the FBI
about an agency PowerPoint presentation
showing how face recognition could be used to
identify people attending the 2008 presidential
campaign rallies for then-senators Barack
Obama and Hillary Clinton.
150
In 2015, the FBI
admitted that it conducted surveillance ights
over Ferguson and Baltimore during protests
of police use of force.
151
e Department of
Homeland Security has monitored Black Lives
Matter protests.
152
And footage of Chris Wilson’s
protest shows an ocer videotaping the event.
153
What if every time an FBI special agent pointed
his camera at a protester, the FBI could use face
recognition to identify her?
e First Amendment protects our freedom
of speech and our right to “peaceably assemble,
and petition the [g]overnment for a redress
of grievances.” Unfortunately, the Supreme
Court’s interpretation of the First Amendment
may provide little protection against the
use of face recognition to identify peaceful
protesters. Despite the fact that leading law
enforcement agencies—including the FBI
and the Department of Homeland Security
(DHS)—have explicitly recognized the potential
chilling eect of face recognition on free speech,
we found that almost none of the agencies
using face recognition have adopted express
prohibitions against using the technology to
track political or other First Amendment activity.
1. First Amendment case law is unclear on
face recognition.
First Amendment case law oers mixed
guidance on whether face recognition
would impermissibly chill free speech and
association.
154
e Supreme Court has held
that the First Amendment protects the right
to anonymous speech and association. But the
Court has also held that the mere surveillance
of speech is insucient grounds for a First
Amendment violation.
a) e right to anonymous speech and
association.
Face recognition, at its core, is a means of
identication. In 1958, the Supreme Court
held in NAACP v. Alabama that the NAACP
could not be compelled by state law to disclose
the identities—the names and addresses—of
its members, on the grounds that disclosure
would likely hinder the ability of those
members collectively to advocate their beliefs.
155
e Court noted that there existed a “vital
relationship between freedom to associate and
privacy in one’s associations,” particularly in
instances where a group advocates minority or
unpopular beliefs.
156
Talley v. California in 1960, and McIntyre v.
Ohio Elections Commission in 1995, rearmed
the protection of anonymous speech. In Talley,
the Court held that a law prohibiting the
distribution of anonymous pamphlets violated
the First Amendment. e Court reasoned
that “[t]here can be no doubt that such an
identication requirement would tend to restrict
freedom to distribute information and thereby
freedom of expression.”
157
“Anonymity,” the
Court said 35 years later in McIntyre, “is the
shield from the tyranny of the majority.”
158
43
b) e right of the police to investigate
demonstrations.
e right to free speech and association
does not amount to a right to be free from
surveillance, however.
159
In Laird v. Tatum in
1972, the Supreme Court considered whether
military surveillance of public meetings and
demonstrations had an “inhibiting eect” on
the expression of First Amendment rights. e
Court held that without a showing of past or
immediate danger of direct injury, it does not.
Since then, two lower federal courts have applied
Tatum to permit police photography of public
demonstrations.
160
But there are limits to this doctrine. In Hassan
v. City of New York, a 2015 case challenging
the NYPD’s pervasive video, photographic, and
undercover surveillance of Muslim Americans
following 9/11, the ird Circuit agreed with
the plaintis—the victims of surveillance—
that the manner by which the program was
administered—specically targeting a group
of people for their beliefs and religious
aliations—may have caused them “direct,
ongoing, and immediate harm.”
162
While photography is a rst step in face
recognition, face recognition is more than
photography; it is identication. As a result,
we do not know what courts will say about the
integration of face recognition into photographic
surveillance of protests.
163
2. Legislators and police forces could x this
problem. ey haven’t.
In the absence of clear protections aorded by
the courts, it is critically important that legislators
and police forces consider the implications of face
recognition on free speech.
“Surveillance has the potential
to make people feel extremely
uncomfortable, cause people
to alter their behavior, and
lead to self-censorship
and inhibition.”
- e International Justice and Public Safety Network
Legislators have yet to step up to this task.
As noted above, not a single state has passed
legislation to comprehensively rein in face
recognition—and none of the laws that have
been passed address the specic risks that face
recognition poses to free speech and expression.
e federal Privacy Act generally prohibits the
government from keeping records “describing
how any individual exercises rights guaranteed
by the First Amendment.”
164
But the FBI is now
petitioning for its face recognition system to be
exempt from the enforcement of this provision.
165
Major federal and state law enforcement
agencies have recognized the threat that face
recognition presents to free speech. A Privacy
Impact Assessment, drafted in 2011 by DHS,
the FBI, and a number of state police agencies,
considered the eects of law enforcement face
recognition on the “erosion or compromise of
anonymity.”
166
e document recognizes that
“surveillance has the potential to make people
feel extremely uncomfortable, cause people to
alter their behavior, and lead to self-censorship
and inhibition.”
167
To address this concern, the Assessment
encourages that law enforcement use policies
include express provisions “concerning the
appropriate use of a facial recognition eld
identication tool in areas known to reect
44
The Perpetual Line-Up
an individual’s political, religious or social
views, associations, or activities.”
168
In such
circumstances, “the collection of long range
lens photographs should be limited to instances
directly related to criminal conduct or activity.”
169
We surveyed many of the state law enforcement
agencies that helped write the Assessment.
170
But only one agency that provided responsive
records expressly addressed the use of face
recognition on First Amendment activities in
its face recognition use policy.
171
Ohio’s newly
implemented rule on face recognition states:
Law enforcement may not employ
this technology to conduct dragnet
screening of individuals, nor should it
use it to facilitate mass surveillance of
places, groups or activities unless doing
so furthers an ocial law enforcement
activity. For example, it would not be
appropriate for law enforcement to use
facial recognition technology to conduct
surveillance of persons or groups based
solely on their religious, political or other
constitutionally protected activities or
aliations unless doing so furthers an
ocial law enforcement activity.
e FBI appears to be implementing the PIA’s
recommendations by directing users of its face
recognition systems to adopt similar rules.
e FBI face recognition database Policy and
Implementation Guide states: “All appropriate
use policies must protect the constitutional
rights of all persons and should expressly
prohibit collection of photos in violation of an
individual’s 1
st
and 4
th
Amendment rights.”
173
However, of the four state law enforcement
agencies that provided their face recognition
use policy and can submit searches to the FBI
face recognition database, not one included this
express prohibition.
174
45
SIDEBAR 5:
SCORING FREE SPEECH PROTECTIONS
Given the potential chilling eect of face recognition on protected
First Amendment activities, we award the highest score on free speech
protections only to agencies that expressly address—and enumerate—
activities that may chill free speech in a face recognition use policy, not
just a general manual or procedural order.
Express statement in a face recognition use policy
prohibiting the use of face recognition to target or collect
information on individuals on the basis of their race,
religion, or other bases that may stie speech.
(1) A statement in a face recognition use policy
prohibiting the use of face recognition in violation of state
or federal law, including the First Amendment; or (2) a
statement in a general policy or police manual prohibiting
the targeting or collection of information on individuals
on the basis of their race, religion, or other bases that may
stie speech.
No statements outlined in either section above.
46
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D. ACCURACY
“With an identication rate
above 95% as measured by
U.S. government-sponsored
Face Recognition Vendor
Tests, our technology is the
industry’s nest.”
- FaceFirst website
“FaceFirst makes no
representations or warranties
as to the accuracy and
reliability of the product in
the performance of its facial
recognition capabilities.”
- FaceFirst contract with the San Diego Association
of Governments
In police face recognition, there are high stakes
to accuracy. An accurate algorithm correctly
identies a face in an ATM photo and leads
police to a robber’s door. An inaccurate
algorithm sends them to the wrong house—and
could send an innocent person to jail.
175
Face recognition companies understand this,
and promise police departments seemingly
sky-high accuracy standards. e website of
FaceFirst, which uses Cognitec’s algorithm in
face recognition software that it sells to police,
states that “[w]ith an identication rate above
95% as measured by U.S. government-sponsored
Face Recognition Vendor Tests, our technology
is the industry’s nest.”
176
is is misleading:
the 95% gure is a decade old and vastly
oversimplies the nuances of accuracy into a
single number from a single test.
177
Since 2006,
Cognitec’s algorithm has doubtlessly changed
dramatically—and the tests have certainly gotten
harder too.
178
In fact, FaceFirst has made sure that it will
not be held to this high standard. A 2015
contract with one of the largest police face
recognition systems in the country, the San
Diego Association of Governments, includes
the following disclaimer: “FaceFirst makes no
representations or warranties as to the accuracy
and reliability of the product in the performance
of its facial recognition capabilities.”
179
Compared to ngerprinting, state-of-the-art
face recognition is far less reliable and well-
tested. Yet other than instructing the recipients
of potential face recognition matches that
search results are only investigative leads—not
conclusive evidence—jurisdictions and other
stakeholders take too few steps to protect against
false positives and other errors.
1. Accuracy remains a work in progress.
Real-time systems and systems with large
databases are especially error-prone.
Face recognition is widely considered to be less
accurate than ngerprint identication.
180
Age
changes faces, as do cosmetics, inebriation, and
obstructions like glasses or hair.
181
Fingerprints,
in contrast, are relatively consistent over time,
although they can be altered by accidents or
prolonged, manual labor.
182
Fingerprinting
has over a century-long track record in law
enforcement. e rst, primitive face recognition
algorithms were developed in the early 1990s.
183
47
When face recognition is run on photos
captured at a distance, it is often subject to a
wider range of environments. In the “wild,”
photos rarely contain the frontal images that
face recognition algorithms prefer. Poor and
uneven lighting can confuse algorithms that rely
on facial features or skin textures. Algorithms
have an especially tough time comparing photos
taken in dierent circumstances, like mug shots
and surveillance camera stills.
184
Real-time, continuous video surveillance
systems tend to combine the worst of these
traits, rendering them less accurate than many
other deployments. Unlike mug shot-based
systems, which use photos captured in controlled
settings according to strict standards, real-time
systems must contend with people going about
their daily lives. Subjects rarely face the camera
straight on, and video stills are often poorly or
unevenly lit. e security cameras themselves vary
in quality and are often mounted on ceilings. ey
often capture only the tops of people’s heads.
In a real-time experiment set in a train station
in Mainz, Germany from 2006 to 2007, lighting
was a major problem. Accuracy was at 60%
during the day but 10–20% at night.
186
A
report on the experiment notes the challenge
of uncontrolled image capture: “cooperative
behavior must be attained from the wanted
person.” Overall recognition rates averaged
between 17% and 29%.
188
Accuracy also drops as databases become
larger.
189
Larger databases are more likely to
contain lookalikes that mislead face recognition
algorithms into picking the wrong matches.
As a database size rises to a national scale, an
algorithm will inevitably encounter highly
similar faces. Larger databases may also be
more likely to contain older images, which can
drive down accuracy.
190
(See Sidebar 6 for an
explanation of accuracy measurements.)
2. Law enforcement agencies use too few
protections for accuracy.
Most agencies that provided a face recognition
use policy included some form of disclaimer
stating that potential matches were investigative
leads only and could not form the sole basis for
arrest. Beyond this, however, agencies appear
to take remarkably few steps to protect against
errors in their face recognition systems.
a) Agencies do not consistently consider
accuracy when purchasing systems.
e contracting process gives agencies a chance
to ensure system accuracy by requiring certain
accuracy thresholds, or that algorithms be
submitted to accuracy tests, both before and
after purchase.
Few agencies provided a full set of contracting
documents in response to our records requests.
Of the nine contract-related responses we
did receive, four were sole source contracts,
meaning that there was no competitive
process for selecting or upgrading the face
recognition system.
193
e other agencies
providing responses demonstrated very dierent
approaches to face recognition accuracy.
On one end of the spectrum, the Los Angeles
County Sheri ’s Department and Ohio Bureau
of Criminal Investigation did not require any
demonstration or testing for face recognition
accuracy. When one company asked whether
there were accuracy expectations for face and iris
recognition, L.A. County responded: “ere are
no expectations, as we are requesting vendors
to enlighten us as to [the] accuracy capability
for standalone face and iris.”
194
ese vague
SIDEBAR 6:
UNDERSTANDING FACE RECOGNITION ACCURACY
e accuracy of a face recognition algorithm cannot be reduced to a single
number. Algorithms make mistakes in a variety of dierent ways, some of
which are more problematic than others. Algorithms use a photo of a subject
(a probe photo) to search for matching faces in a database of identied face
images. An algorithm can return one of two responses: an accept—a photo
that it thinks is a possible match, or a reject—a concession that no matching
photos were found.
• If the algorithm nds a match that indeed
contains the subject, it has achieved a true
accept—it correctly made a match.
• If the subject isn’t in the database of images
and the algorithm correctly returns nothing,
it has achieved a true reject—it correctly
found that there was no match.
• If the subject isn’t in the database of images
but the algorithm mistakenly suggests a
match with the image of someone else, it has
produced a false accept—it matched to the
wrong person.
• If the subject is in the image database,
but the algorithm fails to nd a match or
mistakenly suggests a match containing
someone else, it has produced a false reject—
it should have found the right person but
didn’t.
191
In guring out how to handle false accepts and
rejects, a law enforcement agency has to make a
dicult choice. If the goal is to identify as many
leads as possible, it might prefer to be over-
inclusive and err on the side of false accepts,
giving more possible leads from which to choose
(assuming the correct lead could eventually be
identied from this set).
is is the approach of the FBI face recognition
database (NGI-IPS), which returns between
two and 50 candidate mug shots for any given
search—most of which necessarily will be
false accepts.
192
Yet a false accept could be
devastating to someone mistakenly implicated
by face recognition. To avoid these errors, an
agency might prefer false rejects instead, and
stipulate that searches will return a small number
of candidate images. Doing so, however, risks
failing to nd the right person at all.
49
standards contrast sharply with both agencies’
strong accuracy requirements for ngerprint
algorithms.
195
“[W]e are requesting
vendors to enlighten us as to
[the] accuracy capability for
standalone face and iris.”
- L.A. County Contracting Bulletin
On the other end, for the face recognition
component to its multi-biometric system, the
San Francisco Police Department required that
bidding companies:
• Meet specic target accuracy levels—an error
rate of 1% or better;
• Provide copies of the results from all prior
accuracy tests conducted by NIST in which
their algorithm was evaluated;
• Upon acceptance, submit to verication
tests to ensure the system “achieves the same
or better accuracies than what has been
achieved by relevant NIST and/or other
independent and authoritative 3
rd
party
testing;” and
• Submit to regular future accuracy testing “to
reconrm system performance and detect
any degradation.”
196
South Sound 911 also considered accuracy in its
request for face recognition proposals, requiring
that: “e search results must meet a match rate
of a 96% condence rating,” and “[t]he system
must have high threshold facial recognition
search capability for both in-car and booking
ocer queries.”
197
b) Few agencies used trained human reviewers
to bolster accuracy.
Since face recognition accuracy remains far from
perfect, experts agree that a human must double-
check the results of face recognition searches to
ensure that they are correct. As the architect of
a leading face recognition algorithm put it, “I
wouldn’t like my algorithm to take someone to
jail as a single source” of identifying evidence.
198
Simple human review of results is not enough,
however. Without specialized training, human
reviewers make so many mistakes that overall
face recognition accuracy could actually
drop when their input is taken into account.
Humans instinctively match faces using a
number of psychological heuristics that can
become liabilities for police deployments of
face recognition. For example, studies show that
humans are better at recognizing people they
already know
199
and people of the same race.
200
As evidence of the benets of training, one study
tested the performance of Australian passport
personnel, who use Cognitec’s algorithm to
check for duplicate passport applications.
201
Facial reviewers, who receive limited instruction
in face matching, identied the correct match
or correctly concluded there was no match only
half the time; they did no better than college
students. Specially trained facial examiners,
however, did about 20% better.
Unfortunately, while other agencies may do
this training, documents we received identied
only eight systems that employed human
gatekeepers to systematically review matches
before forwarding them to ocers: the FBI
face recognition unit (FACE Services), the
Albuquerque Police Department, the Honolulu
Police Department, the Maricopa County
Sheri ’s Oce, the Michigan State Police, the
50
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Palm Beach County Sheri ’s Oce, the Seattle
Police Department, and the West Virginia
Intelligence Fusion Center.
202
Even these systems are still not ideal. For all but
two of these systems—the FBI face recognition
unit and the Michigan State Police—the level of
training required for these human gatekeepers is
unclear. Some searches evade review altogether.
When a Michigan State Police ocer conducts
a face recognition search from a mobile phone
(such as for a eld identication during a trac
stop), the algorithms’ results are forwarded
directly to the ocer without any human
review.
203
Similarly, while the FBI subjects its
own searches of its database to trained human
review, states requesting FBI searches of that
same database are returned up to 50 candidate
images without any kind of human review.
204
c) Human reviewer training regimes are still in
their infancy.
Agencies that are eager to implement human
training may encounter yet another diculty:
the techniques for manually comparing photos
of faces for similarity—techniques that would
inform this sort of training—are still in
their infancy. e FBI’s Facial Identication
Scientic Working Group (FISWG), whose
members include academic institutions and law
enforcement agencies at all levels of government,
has developed training and standardization
materials for human facial comparison.
“It’s not science at this
point—it’s more of an art.”
Its preferred approach is “morphological
comparison,” which examines the similarity
of dierent facial features depending on their
“permanence.” However, the science behind
this approach is murky—as FISWG’s materials
report, “only limited studies have been done on
accuracy or reproducibility.”
205
An engineer at
one company was more direct: “it’s not science at
this point—it’s more of an art.”
206
3. Testing regimes are voluntary, sporadic,
and resource-limited.
ere is only one public, independent
benchmark for comparing the accuracy of these
algorithms—a face recognition competition
oered by the National Institute of Standards
and Technology (NIST) every three or four
years. All leading manufacturers currently
submit to these tests, but participation in
the competition is entirely voluntary, and
manufacturers are under no obligation to submit
to NIST tests before selling their algorithms to
law enforcement agencies.
In 2010, NIST observed that accuracy had
improved by “an order of magnitude in each
four-year period” between tests, a dramatic pace
of technological innovation.
207
However, the last
round of testing was in 2013, a lifetime ago at
the pace that face recognition technology moves.
us, state and local law enforcement agencies
seeking to purchase face recognition in 2016
would have less reliable information to go on
when contracting with face recognition vendors
given the time that has passed since the last test.
4. Publicly available photo sets do not
reect the size or diversity of the human
population.
Outside of NIST’s accuracy tests, several
publicly available, academic collections of facial
photos provide a limited basis for making
independent accuracy comparisons between
algorithms. e most prominent of the
51
collections is called “Labeled Faces in the Wild”
and dates to 2007.
208
ese datasets typically
feature celebrities. is makes it easier to label
thousands of individual faces, but fails to capture
the full range of human diversity.
ese datasets are also small, on the order
of a few thousand photos. By contrast, a
single state may have millions of photos in
its face recognition databases. (For example,
Pennsylvania has over 34 million and Michigan
has over 40 million.)
209
e dierence in size
matters: as explained above, as a dataset grows
in size, the likelihood of similar faces increases,
challenging accuracy.
210
A public dataset becomes less useful over time
as researchers calibrate their design decisions
to the specic photos it contains rather than to
face recognition in general. As a consequence,
it is common to see algorithms that perform
awlessly on one dataset but struggle in other
contexts.
211
e Intelligence Advanced Research
Projects Activity (IARPA), a U.S. intelligence
organization that funds intelligence-related
research,
212
is sponsoring an initiative called the
Janus project that will generate a wave of new,
more dicult datasets.
213
52
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As this section explains, an agency can take a variety of steps to safeguard
against errors into their face recognition system. Our accuracy score
considers a range of dierent measures, with particular weight given to the
use of trained human examiners as a backstop to accuracy.
SIDEBAR 7:
SCORING ACCURACY PROTECTIONS
e criteria are:
• Algorithms have been tested by the National
Institute of Standards and Technology;
• Contract with vendor company contains
provisions that require face recognition
algorithms to have been tested for accuracy
and will be tested at all future opportunities;
• Most or all face recognition queries are
validated by trained human examiners or
agencies have a unit or designated personnel
that perform a review and screening function
of the candidate lists (weighted as two
criteria);
• Face recognition results or candidate lists are
treated as investigative leads only.
Agency demonstrates four or ve criteria listed below.
Agency demonstrates three of the criteria.
Agency demonstrates two or fewer of the criteria.
53
E. RACIAL BIAS
“Q: Is the Booking Photo
Comparison System biased
against minorities[?]”
“A: No… it does not see
race, sex, orientation or age.
e software is matching
distance and patterns only,
not skin color, age or sex of an
individual.”
- Frequently Asked Questions, Seattle Police Department
Human vision is biased: We are good at
identifying members of our own race or
ethnicity, and by comparison, bad at identifying
almost everyone else.
214
Yet many agencies using
face recognition believe that machine vision is
immune to human bias. In the words of one
Washington police department, face recognition
simply “does not see race.”
215
e reality is far more complicated. Studies of
racial bias in face recognition algorithms are few
and far between. e research that has been done,
however, suggests that these systems do, in fact,
show signs of bias. e most prominent study,
co-authored by an FBI expert, found that several
leading algorithms performed worse on African
Americans, women, and young adults than on
Caucasians, men, and older people, respectively.
216
In interviews, we were surprised to nd that two
major face recognition companies did not test
their algorithms for racial bias.
217
Racial bias intrinsic to an algorithm may
be compounded by outside factors. African
Americans are disproportionately likely to come
into contact with—and be arrested by—law
enforcement.
218
is means that police face
recognition may be overused on the segment of
the population on which it underperforms. It
also means that African Americans will likely
be overrepresented in mug shot-based face
recognition databases. Finally, when algorithms
search these databases, the task of selecting a
nal match is often left to humans, even though
this may only add human bias back into the
system.
1. Face recognition algorithms exhibit
racial bias.
Despite the lack of extensive public and
independent testing, several studies have
uncovered racial bias in face recognition
algorithms. In 2011, researchers used the
algorithms and images from a 2006 NIST
competition to compare accuracy on subjects
of East Asian and Caucasian descent.
219
ey
found that algorithms developed in East
Asia performed better on East Asians, while
algorithms developed in Western Europe and
the U.S. performed better on Caucasians. is
result suggests that algorithms may be most
accurate on the populations who developed
them—a concerning eect given that software
engineers in the United States are predominately
Caucasian males.
220
e 2012 FBI-coauthored study tested three
commercial algorithms on mug shots from
Pinellas County, Florida.
221
e companies
tested include the suppliers of algorithms to
the Los Angeles County Sheri, the Maryland
Department of Public Safety, the Michigan
State Police, the Pennsylvania Justice Network,
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and the San Diego Association of Governments
(SANDAG), which runs a system used by 28
law enforcement agencies within San Diego
County.
222
All three of the algorithms were 5 to 10% less
accurate on African Americans than Caucasians.
To be more precise, African Americans were
less likely to be successfully identied—i.e.,
more likely to be falsely rejected—than other
demographic groups.
223
A similar decline
surfaced for females as compared to males
224
and younger subjects as compared to older
subjects.
225
In one instance, a commercial algorithm failed
to identify Caucasian subjects 11% of the time
but did so 19% of the time when the subject was
African American—a nearly twofold increase
in failures. To put this in more concrete terms,
if the perpetrator of the crime were African
American, the algorithm would be almost twice
as likely to miss the perpetrator entirely, causing
the police to lose out on a valuable lead.
Depending on how a system is congured,
this eect could lead the police to misidentify
the suspect and investigate the wrong person.
Many systems return the top few matches
for a given suspect no matter how bad the
matches themselves are. If the suspect is African
American rather than Caucasian, the system
is more likely to erroneously fail to identify
the right person, potentially causing innocent
people to be bumped up the list—and possibly
even investigated. Even if the suspect is simply
knocked a few spots lower on the list, it means
that, according to the face recognition system,
innocent people will look like better matches.
5–10%
Lower accuracy rates for African
Americans and women, as measured
in an FBI co-authored 2012 study.
ere are various explanations for this bias.
e simplest is that training is destiny; the
faces that an algorithm practices on are the
faces it will be best at recognizing. When those
faces disproportionately represent one race, an
algorithm will optimize its accuracy for that
group at the expense of others. Notably, in
addition to testing three commercial algorithms,
the 2012 study also tested an academic
algorithm that was trained three separate times
exclusively on Caucasians, African Americans,
and Latinos; it consistently performed best on
the race on which it was trained.
226
e authors of the 2012 study suggest another
contributing factor: Some demographics may
be inherently more dicult to recognize than
others. For example, they hypothesize that
cosmetics could make it harder to match photos
of women.
227
In interviews, several experts noted
that individuals with darker skin tones are more
dicult to identify because face recognition
relies on color contrast to characterize facial
features.
228
Finally, bias may be the inadvertent result of
intentional design decisions. Engineers designing
an algorithm may purposefully design it to
perform on certain demographics, potentially at
the expense of others.
As an example of a design choice that may
result in bias, a 2014 handbook for users of
the Pennsylvania Justice Network (JNET)
face recognition system instructs users on how
55
to generate a three-dimensional model of a
face by using software from a company called
Animetrics. In order to generate the model, users
are instructed to enter the race or ethnicity of
the subject. But as described in the handbook,
the JNET system’s only options are “Generic
Male, Generic Female, Asian Male, Asian
Female, Caucasian Male, Caucasian Female or
Middle Eastern Male.”
229
As of 2015, African
Americans and Latinos comprised 11.7%
and 6.8% of Pennsylvanians, respectively.
230
While this is only one of many tools used in
Pennsylvania’s face recognition software suite,
it excludes a signicant portion of the state’s
population—and, potentially, the communities
most likely to encounter law enforcement.
2. Face recognition algorithms are not being
tested for racial bias.
e scientic literature on racial bias of face
recognition is sparse. e two studies discussed
in this section represent some of the only lines
of work to investigate this phenomenon. NIST,
which has run a face recognition competition
every three to four years since the mid-1990s,
has tested for racial bias just once.
231
e problem may be related to demand: Even
jurisdictions like the San Francisco Police
Department—which required prospective face
recognition vendors to demonstrate a target
accuracy levels, provide documentation of
performance on all applicable accuracy tests, and
submit to regular future accuracy tests—did not
ask companies to test for racially biased error
rates.
232
is state of aairs is not limited to the
government or academia. In the spring of
2016, we conducted interviews with two of the
nation’s leading face recognition vendors for law
enforcement to ask them how they identify and
seek to correct racially disparate error rates. At
that time, engineers at neither company could
point to tests that explicitly checked for racial
bias. Instead, they explained that they use diverse
training data and assume that this produces
unbiased algorithms.
233
Engineers at two of the
nation’s leading face
recognition companies
indicated that they did not
explicitly test their systems
for racial bias.
Figure 11: Pennsylvania Justice Network, “JNET Facial Recognition User Guide Version 1.8” (Dec. 4, 2014)
10.3.4.1.3 Generating the 3D Model
Once the feature points have been adjusted (if necessary), it is time to generate the 3D
model representing each subject.
• Metadata Category: Select the category from the drop-down list that best represents
the subject in the photograph. You can choose, Generic Male, Generic Female, Asian
Male, Asian Female, Caucasian Male, Caucasian Female or Middle Eastern Male.
56
The Perpetual Line-Up
is problem may trace, in part, to a lack of
diverse photo datasets that could be used
to test for racially biased errors. e 2011
study, for example, was likely tested only on
Caucasians and East Asians because its database
was composed of photos of undergraduate
volunteers who were 77% Caucasian, 14%
Asian, and 9% “other or unknown.”
234
Likewise,
the 2012 study also tested the algorithms on
Hispanics, but the results were erratic due to
“the insucient number of training samples
available.”
235
is situation is the norm in face
recognition—diverse collections of photos that
accurately capture communities of interest to law
enforcement are in short supply. is deciency
reduces the reliability of testing regimes for
existing systems and makes it more dicult to
train new algorithms.
3. African Americans are disproportionately
likely to be subject to police face recognition.
A face recognition system can only “nd” people
who are in its database; in systems that rely on
mug shot databases, racial disparities in arrest
rates will make African Americans much more
“ndable” than others—even though those
identications may themselves be more likely to
be erroneous.
Many agencies that reported using mug shot
databases (alone or in conjunction with driver’s
license and ID photos) experience dramatic
Sources: U.S. Census, Minnesota Department of Public Safety, King County Department of Adult and Juvenile Detention, Pennsylvania Uni-
form Crime Reporting System, State of California Department of Justice Oce of the Attorney General, Virginia State Police, Arizona Depart-
ment of Public Safety
236
Ratio of African American arrest rates to population share in
select jurisdictions.
3:1
Arizona
5:1
Minnesota
2:1
Hawaii
3:1
Pennsylvania
3:1
L.A. County
3:1
San Diego
County
2:1
Michigan
2:1
Virginia
racial disparities in arrest rates. For example,
in 2014, African Americans represented 5.4%
of Minnesota’s population but 24.5% of those
arrested. In contrast, Caucasians were 82.1%
of the population but 57% of those arrested.
237
A Center on Juvenile and Criminal Justice fact
sheet notes that “African American women,
5.8% of San Francisco’s total female population,
constituted 45.5% of all female arrests in
2013.”
238
57
ese statistics do not just speak to arrests. ey
reect the fact that African Americans are not
just more likely to be arrested: they are also more
likely to be stopped, interrogated, or otherwise
investigated by law enforcement. Police face
recognition systems do not only perform worse
on African Americans; African Americans also
more likely to be enrolled in those systems and
be subject to their processing.
A natural response to the enrollment problem
might be to move away from mug shot databases
and instead use driver’s license and ID photo
databases, which may better reect the overall
population in a jurisdiction. As this report
explains, however, this results in the creation
of a dragnet biometric database of law-abiding
citizens—a shift that is unprecedented in the
history of federal law enforcement and raises
profound privacy issues. Face recognition
presents some problems for which there are no
easy answers.
SIDEBAR 8:
SCORING PROTECTIONS AGAINST RACIAL BIAS
ere was too little information available to score individual agencies on
their eorts to combat racial bias in their face recognition system. e
main factor in this decision was the absence of regular accuracy tests for
racially biased error rates. (Many jurisdictions also failed to disaggregate
arrest rates along the lines of race and ethnicity.) If NIST institutes
regular accuracy tests for racial bias, however, police departments and
the communities they serve should condition system purchases on an
algorithm’s performance in bias tests.
58
The Perpetual Line-Up
F. TRANSPARENCY &
ACCOUNTABILITY
In 2014, Victor Manuel Torres, a civil rights
attorney in San Diego, began receiving
complaints—about one a day over the course
of a few months. Each caller had been stopped
by the police for a dierent reason, but after
that each story was the same: Sometime during
questioning, the ocer had pulled out a tablet
and taken their photo, without asking for
consent or providing an explanation. e callers
were indignant and confused. Why would the
police need my photo? How were they going to
use it? Was this even legal?
239
Police in San Diego County, California, began
using face recognition to identify subjects in
the eld in 2012. Two years later, there were
over 800 ocers using the mobile systems
from 28 dierent law enforcement agencies
in the region.
240
Yet it wasn’t until three years
after it was deployed, in April 2015, that a face
recognition use policy was put in place. Once
that policy was made public, Torres noted, the
complaints virtually stopped.
What’s unusual about the San Diego case is
not the police’s use of face recognition without
consent or notice—it’s that a use policy was
eventually approved by an advisory board and
then made available to the public.
Most law enforcement agencies have deployed
face recognition with minimum levels of
transparency and internal accountability. Four
of the 52 responsive agencies—or less than
8%—have a face recognition use policy that is
publicly available. For 24 jurisdictions that use
or formerly used face recognition, no use policy
whatsoever was provided in response to our
survey. Just one jurisdiction received legislative
approval for their policy, and one policy received
formal review by an outside privacy or civil
liberties organization. Compounding this lack
of oversight, almost none of the jurisdictions we
surveyed—including the FBI—have a functional
internal audit regime to prevent misuse or
document when it occurs.
1. Law enforcement agencies are not
transparent about using face recognition
Police departments generally tell the public
very little about their use of face recognition.
When the existence of these systems comes to
light, it is often due to the work of investigative
journalists and privacy organizations.
e agencies with the most
advanced face recognition
systems are often the least
transparent.
Ohio’s face recognition system remained
almost entirely unknown to the public for ve
years—until an investigation conducted by
the Cincinnati Enquirer uncovered that the
Ohio’s Bureau of Criminal Investigation had
enrolled all Ohio state driver’s license photos
in the system two months prior, with no notice
provided to current or future license holders.
241
e Sheri ’s Department in Hennepin County,
Minnesota, acquired face recognition in 2013.
Yet it only released information about its system
in 2016 after a court ordered it to disclose that
information to an investigative journalist.
242
Some of the largest law enforcement agencies—
and those who may have the most advanced face
recognition systems—are the least transparent.
e New York Police Department’s use of face
recognition has been described in numerous
news articles; NYPD spokespersons admit that
it exists.
243
Yet the NYPD denied our records
59
request entirely, arguing that the responsive
records fall under the “non-routine techniques
and procedures” exemption in New York’s
Freedom of Information Law.
244
e Chicago
Police Department provided responsive
records to a small fraction of our request. ese
documents suggested that the department
had purchased of a large-scale system in 2012,
but provided no information about how such
a system is used or what policies are in place
governing such use, except that such policies
were to be developed in the future.
245
e Los Angeles Police Department is the only
American police department to openly claim
to use real-time, continuous face recognition.
Curiously, the LAPD found “no records
responsive to [our] request”
246
for information
about this or any other face recognition system,
despite ten years of LAPD news releases and
annual reports that document at least three
separate police face recognition initiatives.
247
Unfortunately, communities aren’t the only ones
in the dark. In criminal litigation, prosecutors
are required to disclose to defense counsel any
evidence that may exculpate the accused; those
disclosures are referred to as “Brady disclosures”
or “Brady evidence,” after the Supreme Court
case that mandated those productions.
248
e
Pinellas County Sheri ’s Oce system has
been operational for almost 15 years. e
Pinellas County Public Defender, Bob Dillinger,
reports that in that time, his oce has never
received any face recognition information as
part of a Brady disclosure.
249
In an interview, he
suggested that if the PCSO face recognition
system ever identies someone other than a
criminal defendant as a potential suspect in that
defendant’s case, public defenders have a right to
know.
250
2. Agencies are even less transparent about
how—or how frequently—they use face
recognition.
While a limited number of agencies inform the
public about the existence of face recognition,
even fewer disclose how ocers use it. Only four
of the agencies we surveyed—the San Diego
Association of Governments (SANDAG), the
Honolulu Police Department, the Michigan State
Police, and the Seattle Police Department—make
their face recognition use policies available to the
public.
251
We are also aware of just one agency that
regularly reports to the public how frequently face
recognition is used.
252
e FBI, for its part, has consistently failed to
comply with the transparency requirements of
the E-Government Act and the Privacy Act,
which mandate that the FBI publish a System of
Records Notice or a Privacy Impact Assessment
when the agency starts to maintain—or
signicantly modies—a database like the Next
Generation Identication system.
253
In 2011,
the FBI gave select state police departments
the ability to run face recognition on photos
in the FBI’s database—yet the FBI didn’t
publish a Privacy Impact Assessment about the
program until 2015. Even though the FBI’s
face recognition database itself was launched in
2008, the FBI didn’t publish a System of Records
Notice about it until this year.
254
ese are not obscure bureaucratic lings. ey
are the means through which the American
public can learn about new government
tracking technology—and hold the government
accountable for going too far. Instead of working
to address these shortcomings, the FBI is now
proposing to exempt its Next Generation
Identication system, which includes its face
recognition database, from provisions of the
60
The Perpetual Line-Up
Privacy Act that guarantee members of the
public access to records that identify them,
information about the sharing of these records,
and judicial review.
255
3. Police policies are not subject to legislative
or public review.
Very few agencies require or obtain legislative
approval of a police face recognition use policy,
or mandate review of that policy by privacy and
civil liberties organizations.
Of the 52 responsive agencies, only SANDAG
appears to have a system in place where elected
ocials review and approve the agency’s face
recognition use policy.
256
e Acceptable Use
Policy for face recognition requires annual
review by SANDAG’s Board of Directors, which
is mostly comprised of local elected ocials.
SANDAG’s Board of Directors meetings are
also open to the public
.257
We found only one agency
that submits its face
recognition use policy for
legislative approval.
e Seattle regional system appears to be the
only one with a policy formally reviewed by an
outside organization. e Seattle City Council
approved funds for the program on condition
that the ACLU of Washington was consulted on
and approved the nal policy.
258
e Michigan
State Police also initiated informal review of
their face recognition use policy by a state
legislator and a contact at the ACLU.
259
No
other jurisdiction we surveyed had any formal or
informal external mechanism in place for review
or approval of their system’s use policy, either
by the legislature or privacy and civil liberties
organizations.
4. Few agencies have robust internal audits to
prevent misuse.
Few police departments seem to have implemented
robust internal accountability measures. Many
agencies, including the Maricopa County Sheri ’s
Oce, the Carlsbad Police Department, the
Maryland Department of Public Safety, and the
Albuquerque Police Department expressly stated
that they had not audited face recognition use.
260
Only nine of the 52 responsive agencies (17%), plus
the FBI face recognition unit (FACE Services),
expressly indicated that they audit their employees’
use of the face recognition system for improper
use.
261
Of these 10 regimes, however, some are non-
operational. e Pinellas County Sheri ’s Oce,
for example, does not audit system use—despite
policies that suggest otherwise. e Standard
Operating Procedure for mobile biometrics
states that “[a]ll [mobile] biometric and search
activity are logged and subject to audit.”
262
e
Palm Beach County Sheri ’s Oce, an agency
that uses the Pinellas County system, also said
that audits are “done by Pinellas.”
263
Nonetheless,
Sheri Gualtieri acknowledged in an interview
that internal audits are not conducted. “We don’t
police our users,” he said.
264
e Pinellas County
face recognition system—which appears to be
the most frequently used face recognition system
in the country—may therefore lack any internal
oversight or accountability mechanism to protect
against, or even to detect, misuse.
Only one responsive agency—the Michigan
State Police—actually provided evidence of
routine audits in response to our request for such
records.
265
61
We developed one score focused on public transparency and oversight
by legislatures or privacy and civil liberties groups (Public Transparency),
and another focused on internal oversight measures (Internal Audits).
e Public Transparency score was based on information provided in
response to document requests and publicly available records, whereas the
Accountability score was based on documents provided by agencies.
PUBLIC TRANSPARENCY.
Has the agency publicly posted its face
recognition use policy, and has it been reviewed
or approved by a legislature or privacy and civil
liberties groups?
Agency has a public face recognition
use policy that has been reviewed or
approved by a legislature and/or privacy
and civil liberties groups.
Agency has a public use policy, but there
is no evidence that the policy received
external review or approval.
Agency has not made its use policy
public, or has no use policy.
INTERNAL AUDITS.
Does the agency monitor and conduct audits
of face recognition use by its ocers and other
accessing agencies? (Since our records request
specically asked for records pertaining to audits,
when an agency did not provide audit records or
sample audit forms and did not deny this request,
it was assumed that no audits were conducted.)
Formal audit procedure is in place
and there is evidence that audits are
indeed conducted.
Audit procedure in place but it is
unclear if audits are conducted.
No audit procedure in place and/or
no audits are conducted.
SIDEBAR 9:
SCORING TRANSPARENCY & ACCOUNTABILITY
PROTECTIONS
62
The Perpetual Line-Up
A. LEGISLATURES
A core recommendation of this report is that
Congress and state legislatures pass common-
sense legislation, comparable to the Wiretap
Act and its state analogs, to comprehensively
regulate law enforcement face recognition. is
legislation should implement the following rec-
ommendations, each of which is featured in the
Model Face Recognition Act (p. 102).
• Recommendation 1. Law enforcement face
recognition searches should be conditioned
on an individualized suspicion of criminal
conduct.
For over two centuries, American law
enforcement has been constrained by a basic
standard: e police cannot search anyone they
please. Rather, before law enforcement ocials
infringe on an individual’s liberty, they generally
must have an individualized suspicion that the
individual is engaged in criminal conduct.
At a minimum, legislatures should require that face
recognition searches be conditioned on an ocer’s
reasonable suspicion that an individual is engaged
in criminal conduct. is standard currently applies
to police investigatory stops.
266
While some states
require that people identify themselves at the
request of police, the Supreme Court has ensured
that those laws require a predicate of reasonable
suspicion.
267
Face recognition allows law
enforcement to identify someone without stopping
or even talking to her. Our proposal ensures that
the old standard survives new technology.
A reasonable suspicion standard should apply
to all Stop and Identify, Arrest and Identify, and
Investigate and Identify searches that run on
mug shot databases. Higher standards should
apply to riskier deployments, such as systems
that rely on driver’s license databases or real-
time, continuous video surveillance.
• Recommendation 2. Mug shot databases
used for face recognition should exclude
people who were found innocent or who
had charges against them dropped or
dismissed.
Mug shot databases used for face recognition
include countless individuals who have
interacted with law enforcement but who
have never been convicted of a crime.
268
is is particularly problematic in cases like
Chris Wilson’s: A single act of peaceful civil
disobedience should not result in a lifetime in a
criminal face recognition database.
A single act of peaceful civil
disobedience should not result
in a lifetime in a criminal face
recognition database.
Congress and states that rely on mug shot
databases for face recognition should follow the
lead of Michigan, which requires the destruction
of biometric data from people who are arrested
but have been found innocent, or who have
VI. RECOMMENDATIONS
63
had the charges against them dropped or
dismissed.
269
e FBI should do this voluntarily,
whether or not Congress commands it to do so.
• Recommendation 3. Searches of license
photos should only occur if state
legislatures vote to allow them. States that
allow access should notify the public.
Unless and until state legislatures openly debate
this access and armatively vote to grant it, law
enforcement face recognition systems should
constrain themselves to mug shot databases.
Even if state legislatures do approve searches
of license and ID photos, many citizens may
remain unaware of this practice. erefore, these
states should implement a notice requirement
similar to that of Washington State, which
requires special notices to driver’s license
applicants, online postings, and “notices in
conspicuous locations” in the Department of
Licensing’s physical oces.
270
• Recommendation 4. Searches of driver’s
license and ID photos should occur only
under a court order issued upon a showing
of probable cause.
States that allow searches of driver’s license
and ID photos should require a higher level of
individualized suspicion, preferably probable cause,
for those searches. If a state scans all of its drivers’
faces as part of criminal investigations, it should, at
a minimum, ensure that those searches are based
on reasonably trustworthy information.
271
e determination of probable cause should
not be in the hands of the police or the FBI.
It should be in the hands of a state judge or a
federal judge with jurisdiction over the state.
As with the Wiretap Act, this judicial oversight
requirement should not be total. In true
emergencies, searches should initially proceed
without judicial approval (but require a follow-
up application). Other scenarios should not
require judicial approval at all. ese include
searches to identify missing children, deceased
victims, and lawfully arrested people during the
booking process.
We also believe that judicial approval should not
be required for police searches that are narrowly
designed to detect identity theft and fraud. ese
searches parallel departments of motor vehicles’
longstanding practice of “de-duping” ID photos
to detect fraud.
• Recommendation 5. Limit searches
of license photos—and after-the-fact
investigative searches—to investigations of
serious oenses.
ere is a tradition in American law
enforcement of limiting the most controversial
investigative techniques to the most serious
crimes.
272
at principle should apply to
face recognition: If a state decides to allow
law enforcement to conduct face recognition
searches of its driver’s license and other ID
photos, it should limit those searches to serious
oenses, preferably those identied in the oral
and wire provisions of the Wiretap Act, and
identity theft and fraud.
273
is principle should also apply to Moderate
Risk deployments, which involve face
recognition against a mug shot database. In a
Stop and Identify deployment, where a police
ocer encounters someone in person, takes
her photo, and uses that photo to run a face
recognition search, the use of the technology
is at least somewhat transparent to the search
subject. In a lawfully initiated ocer encounter,
that ocer’s safety is also in play; he has a need
to know whether he is interacting with a law-
abiding citizen or a wanted felon.
274
64
The Perpetual Line-Up
In an Arrest and Identify search, where someone
is arrested and her mug shot is simultaneously
enrolled and run against a mug shot database,
use of face recognition may or may not be
transparent—but the Supreme Court has
recognized a strong state interest in the reliable
identication of suspects in government custody.
275
In an Investigate and Identify search, where a
suspect is identied after the commission of
the oense from a video still or surreptitious
photograph, none of these interests are at play.
e search is entirely invisible to the subject
and the public at large. Outside of the public
eye, there is a risk that some ocials may use
a minor oense, like jaywalking, as a pretext to
justify a search to identify a peaceful protester—
or an attractive member of the opposite sex.
276
For this reason, we believe that Investigate
and Identify searches—even those limited to
mug shot databases—should be limited to
investigations of felonies.
• Recommendation 6. Real-time video
surveillance should only occur in life-
threatening public emergencies under a
court order backed by probable cause.
When operating through a large network of
street surveillance footage—or, potentially,
police-worn body cameras—real-time,
continuous face recognition would allow law
enforcement to secretly locate people and track
their movements. Real-time video surveillance
oers police the same abilities as do real-time
GPS tracking or access to cell-site location
information, techniques that require court-issued
warrants in a growing number of jurisdictions.
277
A simple warrant is not enough, however. If
deployed pervasively, real-time video surveillance
threatens to create a world where, once you set
foot outside, the government can track your
every move.
Some communities may conclude that real-
time video surveillance is too inaccurate, or too
threatening to civil liberties. Communities that
decide to allow real-time video surveillance
under a probable cause-backed court order
should issue those orders only:
• in life-threatening public emergencies;
• in specic locations for a limited period of
time; and
• upon a showing that law enforcement has
exhausted other means to investigate the
crime.
Most of these restrictions have direct analogs in
the Wiretap Act.
278
Also like the Wiretap Act, if
law enforcement is forced to use real-time video
surveillance without a court order, it should le a
prompt follow-up application to a court.
279
• Recommendation 7. Use of face
recognition to track people on the basis of
their race, ethnicity, religious or political
views should be prohibited.
A statute regulating law enforcement face
recognition should prohibit the use of the
technology to track individuals solely on the
basis of their political or religious beliefs,
or any other conduct protected by the First
Amendment, and prohibit tracking of
individuals solely on the basis of their race,
ethnicity, or other protected status. Without
these prohibitions, there is a real danger that face
recognition could chill free speech or endanger
access to education or public health.
• Recommendation 8. All law enforcement
use of face recognition should be subject to
public reporting requirements and internal
audits.
65
Face recognition is too powerful to be secret. Any
law enforcement agency using face recognition
should be required to annually and publicly
disclose information directly comparable to that
required by the Wiretap Act.
280
is would
include:
1. the number of face recognition searches run;
2. the nature of those searches (i.e. Stop and
Identify, Arrest and Identify, Investigate and
Identify);
3. the crimes that those searches were used to
investigate;
4. the arrests and convictions that resulted from
those searches;
5. the databases that those searches accessed;
6. for real-time video surveillance, the duration
and approximate location of those searches;
and
7. any other information that the jurisdiction
deems appropriate.
ese transparency measures should be coupled
with the logging of all searches and rigorous
audits to prevent and identify misuse.
Face recognition is too
powerful to be secret.
• Recommendation 9. Congress should
provide funding to increase the frequency
and scope of accuracy tests and create more
diverse photo datasets for training.
e National Institute of Standards and
Technology will need new funding to expand
the frequency and scope of its accuracy tests—
particularly to create new testing programs to
prevent algorithmic bias and deepen testing
of real-time face recognition systems. NIST
can also play a role in helping face recognition
companies prevent bias—not just test for it.
With increased funding, NIST may be able
to develop more diverse photo datasets that
companies can use to improve the accuracy of
their algorithms across racial, ethnic, and age
groups.
• Recommendation 10. State and federal
nancial assistance for face recognition
should be conditioned on transparency,
oversight, and accountability.
Many state and local face recognition systems
receive federal nancial assistance.
281
Congress
should use its power of the purse to promote
transparency, public accountability, internal
audits, and accuracy. Federal nancial assistance
should be restricted to federal, state and local
agencies that:
• publicly report use statistics;
• publicly post use policies and obtain approval
for those policies from elected ocials;
• certify that internal audits are in place; and
• participate in NIST accuracy tests, and,
when they are available, tests for racially
biased error rates.
State legislatures can institute similar measures.
B. LAW ENFORCEMENT
Regardless of when legislatures act, there are
a number of steps that federal and state law
enforcement can take to address the problems
presented in this report.
66
The Perpetual Line-Up
1. FEDERAL BUREAU OF INVESTIGATION
& DEPARTMENT OF JUSTICE
• Recommendation 11. Require probable
cause for and restrict searches of driver’s
license and ID photo databases to serious,
enumerated oenses.
e FBI should voluntarily refrain from
searching driver’s license and ID photos in
states that have not passed legislation expressly
authorizing criminal face recognition searches
of those databases. If the FBI decides to proceed
with these searches, it should voluntarily restrict
them to investigations of serious oenses
enumerated in the oral and wire provisions of
the Wiretap Act.
282
In addition, these searches should be limited to
instances in which the FBI has probable cause to
believe that the subject of the search committed
the oense in question. While a probable cause
standard is not common for systems that rely on
driver’s license databases, some major jurisdictions
have adopted it without apparent impediments to
their law enforcement mission.
283
• Recommendation 12. Leverage police
access to the FBI face recognition database
(NGI-IPS) to promote best practices for
face recognition.
At the moment, seven states have the ability to
search the FBI face recognition database, which
is populated by 24.9 million mug shots. Over
time, that number will grow, giving the FBI
an even greater opportunity to promote best
practices for state and local police. In line with
the recommendations set out for legislatures,
ocers should be allowed to search the database
only after certifying that they have a reasonable
suspicion that the suspect in question committed
a felony oense. e FBI should itself adopt this
policy for all mug shot searches.
More broadly, access to the FBI face recognition
database should be conditioned on an agency’s
adoption of a face recognition use policy, public
posting of that policy, and its approval by a city
council or local legislature. ey should also be
contingent on audits.
• Recommendation 13. Audit police and
FBI searches of the FBI face recognition
database and FBI searches of state driver’s
license and ID photo databases.
In a 2012 Senate hearing on FBI use of face
recognition, then Deputy Assistant Director
of FBI Criminal Justice Information Services
(CJIS), Jerome Pender, assured the public that
state agencies’ use of the FBI’s face recognition
database would be audited—both by the
FBI and the state agency in accordance with
agreements signed with those agencies.
284
e
Privacy Impact Assessment for the FBI’s face
recognition database also says that “robust
audit processes [are] already present,” and that
agencies requesting searches “will be subject
to training and audit requirements by the
applicable CJIS Systems Agency (CSA) and
periodic FBI audits.”
285
e 2016 GAO report
revealed, however, that the FBI had never
audited state agency searches of the FBI’s face
recognition database—nor had the FBI audited
its own use of state databases.
286
Going forward,
these audits should be conducted.
Despite the FBI’s assurances in the 2012
hearing, the signed agreements that have
been made public do not require that states
annually audit their own use of the FBI face
recognition database. If it is not now in place,
67
this requirement should be added to all MOUs
between state agencies and the FBI.
• Recommendation 14. e FBI should test
its face recognition system for accuracy and
racially biased error rates, and make the
results public.
e last public accuracy statistics for the
FBI face recognition database suggest that it
successfully includes the correct candidate in
a list of 50 potential matches only 86% of the
time.
288
In other words, in this test, one out
of every seven searches returned a list of 50
“innocent” candidates. is test was run on
a database 25 times smaller than the current
FBI face recognition database; generally, errors
increase with database size.
is is not acceptable. e FBI should regularly
test its system for accuracy and make those
results public. It should do the same for racially
biased error rates.
• Recommendation 15. Investigate state and
local agencies’ use of face recognition for
potential disparate impact.
e Department of Justice Civil Rights
Division regularly investigates state and local
police practices. ey should extend those
investigations to explore face recognition, as the
systems used by a number of agencies under
recent investigation may produce a disparate
impact on minority communities.
In Maricopa County, Arizona, for example—the
subject of a recently settled DOJ civil rights
lawsuit
289
—the Maricopa County Sheri ’s
Oce (MCSO) enrolled all of Honduras’
driver’s license photos into its face recognition
system as part of an eort to combat Mara
Salvatrucha, a Central and North American
gang.
290
African Americans are themselves likely
overenrolled in the system, which also searches
mug shots. In Arizona, African Americans are
arrested at a rate close to three times that of
their share of the population.
291
• Recommendation 16. Develop
procurement guidance for state and local
agencies purchasing face recognition
programs with DOJ funding.
DOJ can support state and local accountability
eorts by providing procurement guidance for
agencies receiving DOJ funding. is guidance
should discourage the use of sole source
contracting for initial purchases or heighten sole
source justication requirements. It could also
encourage: (1) including specic target accuracy
levels in agency Requests for Proposals (RFPs);
(2) requiring proof of participation in NIST
accuracy tests; (3) accuracy verication testing
during the system acceptance process; and (4)
regular independent accuracy tests during the
contract period, including internal tests and
submission to all applicable NIST tests during
that period.
• Recommendation 17. Reverse the
current proposal to exempt the FBI’s face
recognition system from key Privacy Act
requirements.
If promulgated, this rule would eliminate
key mechanisms for public transparency and
accountability over a database already operating
largely in the dark. is report strongly suggests
that we need more transparency over face
recognition, not less. e DOJ and FBI should
reverse this proposal.
292
• Recommendation 18. Voluntarily release
detailed public reports on the FBI’s face
recognition programs.
68
The Perpetual Line-Up
ese reports would detail the databases that
the FBI searches, the number and nature of
face recognition searches conducted by the FBI
and states accessing the FBI system, arrests and
convictions stemming from those searches, and
the types of crimes investigated. e annual
release of this information would add a layer
of public transparency and accountability to
complement internal audits. ese reports could
be modeled after the annual reports required
under the Wiretap Act.
293
2. STATE & LOCAL LAW ENFORCEMENT
• Recommendation 19. Impose a
moratorium on face recognition searches
of state driver’s license and ID photos until
state legislatures regulate that access.
Many states have driver’s privacy laws that allow
law enforcement to access driver’s license and
ID photos; we identied only two, however,
that expressly allow law enforcement to run
face recognition searches of those photos.
294
State and local law enforcement should impose
a moratorium on these searches until state
legislatures have the opportunity to debate and
regulate them through legislation.
• Recommendation 20. Adopt public face
recognition use policies that have received
legislative review and approval.
All agencies, including those that access
another agency’s system, should adopt a face
recognition use policy, preferably in line with the
recommendations set out above. Policies should
be developed simultaneous to—if not before—
the implementation of a face recognition system
or upon gaining access to another agency’s
system. Most importantly, they should be
made public and submitted for approval by city
councils or other local legislative bodies.
A Model Police Face Recognition Use Policy,
based on best practices in existing polices around
the country, is included at the end of the report.
• Recommendation 21. Use contracts and the
contracting process to maximize accuracy.
Agencies should avoid sole source contracting
for initial purchases. In a competitive RFP,
agencies should require vendor companies to
demonstrate target accuracy levels and prove
an algorithm’s submission to NIST accuracy
tests. e system acceptance process should
include accuracy verication testing on searches
that mimic the agency’s actual use of face
recognition—such as on probe images that are
of lower quality or feature a partially obscured
face. Final contracts should require continued
internal accuracy testing in operational settings
and submission to all applicable NIST tests.
Finally, agencies should avoid contracts where
the vendor has disclaimed responsibility for the
accuracy of the algorithm, even when the vendor
uses a third-party algorithm.
• Recommendation 22. Implement internal
audits, tests for accuracy and racial bias,
and the use of trained face examiners.
Law enforcement agencies should audit their
ocers’ use of face recognition, regardless of
whether the agency runs its own system or
accesses another’s. ey should regularly test
their systems for accuracy and, when the tests
become available, racial bias. Each search should
be conducted or reviewed by trained facial
examiners to minimize algorithm error and
possible bias in the search results.
C. NATIONAL INSTITUTE OF
STANDARDS & TECHNOLOGY
• Recommendation 23. Regularly include
tests for algorithmic bias along the lines of
69
race, gender, and age in face recognition
competitions.
NIST’s assessments are widely considered to
be the gold standard in face recognition testing,
yet NIST has checked for racial bias only once
despite holding Face Recognition Vendor Test
(FRVT) competitions for more than a decade.
is information would be of immense value to
law enforcement agencies that look to acquire
face recognition technology.
• Recommendation 24. Increase the
frequency of face recognition competitions,
ideally testing on an annual or biennial
basis.
New innovations, such as deep learning or
drastic reductions in template sizes, can
reshape the technological landscape seemingly
overnight. NIST’s current testing regime, which
oers a competition every three to four years,
is too infrequent to keep up with the pace of
innovation. Acknowledging resource limitations,
NIST should ideally hold its competition every
one to two years.
• Recommendation 25. Continue to update
tests to reect state-of-the-art advances in
face recognition and mobile biometrics.
Face recognition technology continues to
advance. To NIST’s credit, it has kept pace
with these developments, oering a Face in
Video Evaluation (FIVE) competition that is
currently in progress. NIST should continue
to monitor developments in face recognition
and test accordingly. For live video streams,
we recommend that, in addition to testing for
accuracy, NIST should emphasize measuring
computational resource consumption—the
constraint that researchers have noted to be the
technology’s primary limiting factor.
• Recommendation 26. Develop tests that
closely mirror law enforcement workows,
and issue best practices for accuracy
testing.
Law enforcement agencies ask face recognition
algorithms to perform a wide variety of tasks; an
algorithm that excels at one task may struggle
at another. NIST should strive to ensure that
FRVT competitions explicitly test algorithms
in ways that mimic each of these real-world law
enforcement workows, ensuring that agencies
can make informed procurement decisions
tailored to their intended use-cases. Since NIST
tests are considered the gold standard among
both researchers and companies, we recommend
that NIST establish a standards or best practices
document to assist other organizations or law
enforcement agencies that wish to test face
recognition algorithms.
• Recommendation 27. Develop and
distribute diverse datasets of photos.
Researchers and engineers universally complain
about the lack of large, high-quality, diverse
datasets of faces. NIST, along with other
government eorts (such as the IARPA Janus
project), is well placed to take the lead in
developing and distributing such data, which
would both aid algorithm design and provide a
continual source of independent benchmarks.
D. FACE RECOGNITION COMPANIES
• Recommendation 28. Internally check for
algorithmic bias along the lines of race,
gender, and age.
Neither of the major face recognition companies
that we interviewed in the spring of 2016
could point to an internal test that specically
checked for racial bias. Companies should
develop tests to measure this bias. Furthermore,
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companies should work to nd the sources of
this bias, mitigate it where possible, and inform
law enforcement agencies when it cannot be
eliminated completely.
• Recommendation 29. Submit to public,
independent accuracy competitions and
publish performance results using modern,
publicly available datasets.
Public, independent accuracy tests by NIST
and the University of Washington oer the
only basis for comparing the performance
of face recognition algorithms. Companies
should continue to submit their algorithms
for these tests; they should also voluntarily
publish performance results for modern,
publicly available benchmarks that can serve
as an additional basis for comparison. Some
companies, such as Cognitec,
295
have done so
in part, but only for older datasets such as the
NIST Color FERET Database,
296
which was
created in 1996.
E. COMMUNITY LEADERS
• Recommendation 30. Press local and state
police departments and the FBI to be
transparent and adopt policies to protect
privacy, civil liberties, and civil rights.
Face recognition systems cost money. Taxpayers
are paying the bill. ey have a right to know
how those systems are being used, and demand
that they respect their privacy, civil liberties, and
civil rights.
Community leaders should press state and local
agencies, and the FBI, to be fully transparent
about how they use face recognition; if those
agencies refuse, advocates should use state and
federal Freedom of Information laws to take
them to court. Advocates should also press city
councils, state legislatures, and law enforcement
for laws and use manuals that protect individual
liberties and civil rights.
e City and State Backgrounders summarize
face recognition systems in 25 dierent
jurisdictions plus the FBI and cite to the original
documents from those agencies. Whether or not
a backgrounder is available, citizens should ask
their elected ocials or local law enforcement
agency the following questions:
1. Who is enrolled in the police face
recognition database? Is it built from mug
shots, driver’s license and ID photos, or other
sources? If mug shots are used, do police
eliminate photos from cases involving no-
charge arrests or not guilty verdicts? If they
use driver’s license and ID photos, are people
notied of this at the DMV?
2. Who can search the face recognition
database? Can other local, state, or federal
law enforcement agencies (like the FBI)
search or request searches of the system?
3. What kinds of face recognition searches are
run? Do they use it to identify people they
arrest or stop on patrol? Do they use it to
identify criminal suspects from surveillance
video footage? Do they have plans to use face
recognition to identify people in real-time
from live surveillance video?
4. Does the agency have a face recognition
use policy? If not, why not?
5. What legal requirements must be met
before ocers run a face recognition
search? Does an ocer at least need a
reasonable suspicion that someone is
involved in a crime before he can run a
search to identify that person? Or can
ocers run a search on anyone, so long as
it is for a law enforcement purpose? Do
71
searches of license and ID photos require a
higher standard, like probable cause? Will
the agency require warrants for real-time
searches on live surveillance video?
6. Is the agency’s face recognition use policy
available to the public? Was it approved by
a city council or other elected ocials? Did
privacy and civil liberties groups review it?
7. How does the agency ensure that its face
recognition system is accurate? Has the
company submitted its algorithm to accuracy
tests conducted by the National Institute
of Standards and Technology? Does the
purchase contract require certain accuracy
thresholds and require ongoing accuracy
tests in operational conditions? Are all
candidate matches screened by specially
trained human examiners whose decisions
are peer reviewed?
8. How does the agency ensure that its face
recognition system is not biased on the
basis of race, gender or age? Has the agency
tested the system to make sure it is not
biased against certain demographic groups?
Has the agency asked its face recognition
vendor about this possibility, and if so, what
steps has the vendor taken to address this
problem?
9. How does the agency’s face recognition use
policy protect free speech? Does the policy
expressly prohibit using the technology
to identify individuals based solely on
their political or religious beliefs or their
membership in a racial or ethnic minority, or
is this in a separate, general document? Does
the policy allow face recognition to be used
near schools and hospitals?
10. How does the agency stop and detect
misuse and abuse? Does it log all searches
and audit them? If not, why not?
For law enforcement face recognition to be
brought under a reasonable system of regulation,
communities need to ask questions—and take
action.
72
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On September 17, 2016, a week after the 15
th
anniversary of the September 11
th
attacks, New
Jersey and the New York City region were
shaken by two bombings and gripped with the
fear of more to come. A suspect, Ahmad Khan
Rahami, was apprehended just two days after
the initial attack in Seaside Park, New Jersey.
In the press, news emerged that a powerful
technology had been used in the investigation:
face recognition.
297
It is unclear if face recognition actually helped
investigators nd Rahami.
298
But the attacks
in New York and New Jersey raise an urgent
question: In regulating law enforcement use
of face recognition, will we blunt our ability to
respond quickly and eectively to threats to our
safety?
We believe that the answer to this question is
clearly “no.” A core conclusion of this report is
that deployments of face recognition are diverse
and dierentiable. Face recognition can and
should be used to respond to serious crimes and
public emergencies. It should not be used to scan
the face of any person, at any time, for any crime.
e regulatory scheme that we propose will
allow communities to enforce that dierence.
Face recognition is not a monolith. Certain
uses of the technology present fewer risks and
conform to longstanding police practice. But as
face recognition advances, it creates profound
questions about the future of our society.
Are we comfortable with a world where face
recognition is used to identify someone who
police ocers have legally stopped or arrested, or
where it is used, in emergencies, to locate violent
criminal suspects and terrorists? Perhaps.
Are we comfortable with a world where anyone
with a driver’s license is automatically enrolled in
a virtual, perpetual line-up? Are we comfortable
with a world where the government can nd
anyone, at any time, by scanning the faces of
people on the sidewalk? Are we comfortable
with a world where this technology is less
accurate on African Americans, yet more likely
to be used to try to identify them?
As face recognition advances,
it creates profound questions
about the future of our society.
Technology will not wait for us to answer these
questions. Neither will law enforcement. Yet
state legislatures and Congress have not passed
a single law to comprehensively regulate police
use of face recognition—and the Supreme Court
has never formally recognized a right to privacy
in public. With little to guide them, most—
though not all—police departments have not
taken adequate steps to rein in this surveillance
technology.
VII. CONCLUSION
73
In a rapidly evolving world, technology often
outpaces privacy law. It is time for privacy law to
catch up. Achieving this will require the action
of all stakeholders. Privacy protections will not
succeed if they are imposed unilaterally without
the involvement of law enforcement, face
recognition experts, and community leaders. It is
time to enact 21
st
century privacy protections for
a 21
st
century surveillance technology.
74
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VIII. ACKNOWLEDGEMENTS
is report would not be possible without the
foundational research and advocacy of the
American Civil Liberties Union, the Electronic
Frontier Foundation, and the Electronic Privacy
Information Center; the oversight actions of
Senator Al Franken of Minnesota and the
Government Accountability Oce; and the
foresight of Professor Laura Donohue of
Georgetown University Law Center, who also
serves as a faculty director of the Center. We are
grateful for the pioneering work of Upturn and
the Leadership Conference on Civil and Human
Rights, whose scorecard to evaluate the civil
rights impact of police-worn body cameras was a
model for this report.
Many of the ideas in this report were developed
in the course of the 2015 and 2016 Georgetown
Law/Massachusetts Institute of Technology
privacy practicums. We owe a particular debt to
the two student teams, comprised of law students
and engineers, that each spent a semester
exploring regulatory solutions to the thorny
problems that face recognition presents: Kelly
Singleton, Benjamin Tidor, and Madars Virza
(2016); and Shirley Chen, Camille Fischer, Leah
Rabkin, and Harrison Rudolph (2015). In fact,
the title of this report, "e Perpetual Line-Up,"
was coined by the 2016 team, who paraphrased
a passage from a 2013 Washington Post article
by Craig Timberg and Ellen Nakashima. Ben
Sobel contributed key insights and ideas to a
predecessor to this report.
In a talented team of researchers, Katie Evans,
Edward George, Moriah Daugherty, Sabrina
McCubbin, Harrison Rudolph, and Ilana
Ullman went above and beyond and were
central to the development and execution of this
report. Critical guidance and close reading were
provided by Professors Paul Ohm and David
Vladeck, both Center faculty directors, and
Professor Andrew Ferguson of the University
of the District of Columbia David A. Clarke
School of Law. e remainder of our expert
reviewers will remain anonymous, but we are
deeply thankful for their time and attention to
this eort.
Rootid, our design and web development
rm, was the ideal partner: Smart, fast, and
sharp. Often, their work inspired our work and
ambitions.
Our communications rm, Spitre Strategies,
was rst-rate.
e Center on Privacy & Technology at
Georgetown Law is supported by the Ford
Foundation, the Open Society Foundations,
the Georgetown University Law Center, the
Media Democracy Fund, and the Google Policy
Fellowship.
Most importantly, we are grateful to the men
and women in police departments around the
country who provided us with detailed records
on how their agencies use face recognition and
took the time to answer our detailed follow-up
questions.
75
1. See Sara DiNatale, Two years after Andrew Joseph III, 14,
died outside the Florida State Fair, his parents' ght is just
starting, Tampa Bay Times (Feb. 4, 2016),
2. Interview with Chris Wilson ( July 29, 2016) (notes on le
with authors); See also WFTS Webteam, Black Lives Matter
protesters arrested at Florida State Fair, WFTS Tampa Bay
(Feb. 8, 2016), http://www.abcactionnews.com/news/local-
news/black-lives-matter-protesters-arrested-at-state-fair.
3. In September 2014, the Hillsborough County Sheri ’s
Oce (HCSO) nalized a Memorandum of Understanding
with the Pinellas County Sheri ’s Oce (PCSO) to enroll
all existing and future mug shot photos in the PCSO face
recognition database. See the Hillsborough County Sheri ’s
Oce, Memorandum of Understanding, Document. 014030–
014034 at 014031. PCSO sta conrmed that all new
mug shots taken by the HCSO are enrolled in the PCSO
database. See Correspondence between Jake Ruberto, Technical
Support Specialist, Pinellas County Sheri ’s Oce, and Clare
Garvie ( July 28, 2016), Document p. 016831. e FBI
has conrmed that Chris Wilson’s arrest record is in the
Next Generation Identication database, but has refused
to indicate whether her mug shot was enrolled in the FBI’s
face recognition database, the Interstate Photo System.
Florida is one of seven states that have the ability to search
the Interstate Photo System. See U.S. Gov’t Accountability
Oce, GAO-16-267, Face Recognition Technology: FBI
Should Better Ensure Privacy and Accuracy 13 (May 2016).
4. e GAO found that the FBI alone conducted 118,490
face recognition searches of its face recognition database,
the Next Generation Interstate Photo System (NGI-IPS)
from December 2011 to December 2015, and that states
authorized to access to the system conducted 20,000
searches in the four years ending in December 2015.
See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 49, 12 (May 2016). e Pinellas County
Sheri ’s Oce (PCSO) system is searched by 242 local,
state and federal agencies around 8,000 times a month.
See Pinellas County Sheri ’s Oce, Face Analysis
Comparison & Evaluation System: FACES Training 2015,
Document p. 014383–014417 at 014396.
5. See Brendan Klare et. al, Face Recognition Performance:
Role of Demographic Information, 7 IEEE Transactions
on Info. Forensics and Sec. 1789, 1789 (Dec. 2012),
https://assets.documentcloud.org/documents/2850196/
Face-Recognition-Performance-Role-of-Demographic.
pdf. In the report, co-author Richard W. Vorder Bruegge
is identied as the FBI’s subject matter expert for face
recognition. Id. at 1801.
6. See Ernest J. Babcock, Privacy Impact Assessment for the
Next Generation Identication (NGI) Interstate Photo
System, Federal Bureau of Investigation, Department of
Justice (Sept. 2015), https://www.fbi.gov/services/records-
management/foipa/privacy-impact-assessments/interstate-
photo-system (explaining that absent the request of a
submitting agency or a court order, photos will be retained
until the subject is 110 years of age, “or seven years after
notication of death with biometric conrmation”); Pinellas
County Sheri's Oce, Interview with PCSO Sheri Bob
Gualtieri and Technical Support Specialist Jake Ruberto ( July
26, 2016) (notes on le with authors) (explaining that the
PCSO system retains photos indenitely in the absence of
a court order).
7. See Figure 5 and accompanying text.
8. See generally Joseph N. Pato and Lynette I. Millett, eds.,
Biometric Recognition: Challenges and Opportunities, 36–45
(National Academies Press 2010) (hereinafter “Pato
Report”).
9. See Findings: Racial Bias.
10. Technically, biometric technology also analyzes human
behavior, such as gait or keystroke patterns. is report will
not focus on this aspect of biometrics. See Pato Report
at 18 (dening biometrics as “the automated recognition
of individuals based on their behavioral and biological
characteristics.”).
11. See omas Gryta, Americans Keep eir Cellphones
Longer, Wall Street Journal (Apr. 18, 2016), http://www.
wsj.com/articles/americans-keep-their-cellphones-
longer-1461007321 (average phone replaced every 28
months in Q4 2015, according to Citigroup); Average Age of
Light Vehicles in the U.S. Rises Slightly in 2015 to 11.5 years,
IHS Reports, IHS Markit ( July 29, 2015), http://press.ihs.
com/press-release/automotive/average-age-light-vehicles-
us-rises-slightly-2015-115-years-ihs-reports (average length
of new vehicle ownership is 77.8 months, used vehicle
ownership 63 months).
12. See Soweon Yoon and Anil K. Jain, Longitudinal study of
ngerprint recognition, 112 Proc. of the Nat’l Acad. of Sci.
8556 ( July 14, 2015) (establishing the stability of high
quality ngerprints for at least 12 years; citing anecdotal
belief in stability of ngerprints over a lifetime).
13. See generally Driver’s License Renewal, American Automobile
Association, http://drivinglaws.aaa.com/tag/drivers-license-
renewal/ (last visited Sept. 23, 2016) (showing that most
states require driver’s licenses to be renewed every four to
eight years).
14. See e.g., Ga. Code Ann., § 16-11-38 (2010); La. Stat. Ann. §
14:313 (2011); N.Y. Penal Law § 240.35 (McKinney 2010);
Va. Code Ann. § 18.2-422 (2006).
IX. ENDNOTES
76
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15. See Laura K. Donohue, Technological Leap, Statutory Gap,
and Constitutional Abyss: Remote Biometric Identication
Comes of Age, 97 Minn. L. Rev. 407, 415 (2012). Professor
Donohue notes that face recognition is just the rst of a
new generation of biometrics—including iris identication
and gait analysis—that allow for RBI. Id. Of all of these
technologies, however, face recognition is by far the most
widely deployed.
16. Matthew Turk & Alex Pentland, Eigenfaces for
Recognition, 3 J. Cognitive Neurosci. 71, 72 (1991); See
also A. Jay Goldstein, Leon D. Harmon, & Ann B. Lesk,
Identication of Human Faces, 59 Proc. of the IEEE 748, 748
(1971). Attempts to automate aspects of face recognition
go back decades earlier, but these techniques were not fully
automated. Goldstein et al. asked human jurors to examine
images and manually identify and classify facial features
(such as “eyebrow eight” and “chin prole”) on scales from
1 to 5. A computer was then given a description of a target
in the form of a list of features and asked to use the juror’s
coding to nd a match. In contrast, Turk and Pentland
created a computer program that automatically compares
images of faces.
17. e task of sifting through dozens of high-resolution video
frames each second and checking the faces that are found
against databases of hundreds (let alone millions) of photos
demands an enormous amount of expensive computing
power. In the absence of this computing infrastructure,
the video footage might need to be stored and processed
minutes, hours, or even weeks later. Even when this
infrastructure is available, the results will never be provided
to an ocer instantaneously—each step of the process,
from recording the image and transmitting it to a computer
to the face recognition itself takes a small but appreciable
amount of time.
18. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure
Privacy and Accuracy 7, 15 (May 2016) (the Facial Analysis,
Comparison, and Evaluation (FACE) Services unit began
supporting investigations in August 2011).
19. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 46 (May 2016). It also contains almost 5
million “civil photos,” including photos submitted to the
FBI for employment or immigration background checks,
although these photos are not searched unless they are
matched to people already enrolled in the criminal le. See
generally Ernest J. Babcock, Privacy Impact Assessment for
the Next Generation Identication (NGI) Interstate Photo
System, Federal Bureau of Investigation, Department of
Justice (Sept. 2015), https://www.fbi.gov/services/records-
management/foipa/privacy-impact-assessments/interstate-
photo-system.
20. U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 12–13 (May 2016).
21. U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 47–48 (May 2016).
22. See Federal Highway Administration, U.S. Department of
Transportation, Highway Statistics (Sept. 2015), http://
www.fhwa.dot.gov/policyinformation/statistics/2014/pdf/
dl22.pdf.
23. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 48 n. c (May 2016); U.S. Department of State,
Bureau of Consular Aairs, U.S. Passports & International
Travel: Passport Statistics, https://travel.state.gov/content/
passports/en/passports/statistics.html (last visited Sept. 21,
2016) (showing that as of 2015, there are 125,907,176 valid
U.S. passports in circulation).
24. Compare U.S. Gov’t Accountability Oce, GAO-16-
267, Face Recognition Technology: FBI Should Better
Ensure Privacy and Accuracy 51 (May 2016) (uncorrected
copy, on le with authors) with U.S. Gov’t Accountability
Oce, GAO-16-267, Face Recognition Technology: FBI
Should Better Ensure Privacy and Accuracy 51 (May 2016)
(corrected copy) http://www.gao.gov/assets/680/677098.
pdf.
25. See Privacy SOS, In bizarre reversal, FBI suddenly claims it is
not negotiating with states over face recognition access, ACLU
of Massachusetts (Aug. 10, 2016), https://privacysos.org/
blog/fbi-changes-tune-about-face-recognition-and-state-
rmvs/.
26. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 18–32 (May 2016).
27. See Privacy Act of 1974; Implementation, 81 Fed. Reg.
27288, 27289 (proposed May 5, 2016) (to be codied at
28 C.F.R. pt. 16); Center on Privacy & Technology et. al.,
Comment on Proposed Rule to Exempt Next Generation
Identication System from Provisions of the Privacy
Act and the Modied System of Records Notice for
that System ( July 6, 2016), https://www.regulations.gov/
document?D=DOJ-OPCL-2016-0008-0114 (explaining
the impact of the proposed exemptions).
28. See below Section for a list of all agencies we surveyed,
grouped by type of response received.
29. See below Sections: Fourth Amendment and Findings: First
Amendment for a discussion on the Fourth Amendment
and First Amendment implications of law enforcement face
recognition.
30. See William J. Cuddihy, e Fourth Amendment: Origins and
Original Meaning 602–1791 537–548 (2009).
31. See U.S. Const. amend. IV.
32. U.S. Const. amend. I; see generally NAACP v. Alabama ex
rel. Patterson, 357 U.S. 449 (1958); Talley v. California, 362
U.S. 60 (1960); McIntyre v. Ohio Elections Comm’n, 514
U.S. 334 (1995).
33. See Findings: Free Speech.
34. See generally Privacy and Civil Liberties Oversight Board,
Report on the Telephone Records Program Conducted Under
Section 215 of the USA PATRIOT Act and on the Operations
of the Foreign Intelligence Surveillance Court 21-37 ( Jan.
23 2014). A search of a telephone number within the
database required one of 22 NSA ocials to nd that there
77
is a “reasonable, articulable suspicion” that the number is
associated with terrorism. Id. at 8–9. However, the NSA did
not comply with this requirement for a number of years. See
Memorandum Opinion, Foreign Intelligence Surveillance
Court (2009) ( Judge Bates) at 16 n. 14, https://assets.
documentcloud.org/documents/775818/sc-opinion-
unconstitutional-surveillance-0.pdf (“Contrary to the
government’s repeated assurances, NSA had been routinely
running queries of the metadata using querying terms that
did not meet the required standards for querying.”).
35. See Ellen Nakashima, NSA’s bulk collection of Americans’
phone records ends Sunday, Washington Post (Nov. 27,
2015), https://www.washingtonpost.com/world/national-
security/nsas-bulk-collection-of-americans-phone-records-
ends-sunday/2015/11/27/75dc62e2-9546-11e5-a2d6-
f57908580b1f_story.html.
36. See Susan Freiwald, First Principles of Communications
Privacy, 2007 Stan. Tech. L. Rev. 3, ¶62 (2007).
37. See e.g., Tracey v. State, 152 So.3d 504, 515 (Fla. 2014) (“[T]
he federal courts are in some disagreement as to whether
probable cause or simply specic and articulable facts are
required for authorization to access [historical cell-site
location information].”); United States v. Espudo, 954 F.
Supp. 2d 1029, 1038–39 (S.D. Cal. 2013) (noting that a
signicant majority of courts “has found that real-time cell
site location data is not obtainable on a showing less than
probable cause.”).
38. See Geolocation Technology and Privacy: Before the H. Comm.
on Oversight and Gov’t Reform, 114th Cong. 2–4 (2016)
(statement of Richard Downing, Acting Deputy Assistant
Attorney General, Department of Justice) (explaining the
practice of obtaining warrants prior to requesting real-time
GPS records from wireless carriers, the policy of obtaining
warrants prior to use of cell-site simulators, and the practice
of obtaining historical cell-site location records upon a
lower showing of “specic and articulable facts” that records
are sought are relevant and material to an ongoing criminal
investigation).
39. Francis Galton, Finger Prints 2 (1892).
40. Maryland v. King, 133 S. Ct. 1958, 1988 (2013) (Scalia, J.,
dissenting) (citing United States v. Kincade, 379 F.3d 813,
874 (9th Cir. 2004)).
41. Ellen Nakashima, FBI wants to exempt its huge ngerprint
and photo database from privacy protections, Washington Post
( June 1, 2016) (“According to gures supplied by the FBI,
43 percent of all federal arrests and 52 percent of all state
arrests—or 51 percent of all arrests in NGI—lack nal
dispositions, such as whether a person has been convicted or
even charged.”)
42. Police use of eld ngerprint identication began in 2002,
around the time when the rst police face recognition
systems were deployed. See Minnesota police test handheld
ngerprint reader, Associated Press (Aug. 17, 2009), http://
usatoday30.usatoday.com/tech/news/techinnovations/2004-
08-17-mobile-printing_x.htm.
43. Field identications have triggered some of the few
community complaints about face recognition reported in
national press. See Timothy Williams, Facial Recognition
Moves from Overseas Wars to Local Police, N.Y. Times (Aug.
12, 2015); Ali Winston, Facial recognition, once a battleeld
tool, lands in San Diego County, Center for Investigative
Reporting (Nov. 7, 2013).
44. See Findings: Transparency and Accountability.
45. Pinellas County Sheri's Oce, Interview with PCSO
Sheri Bob Gualtieri and Technical Support Specialist Jake
Ruberto (July 26, 2016) (notes on le with authors).
46. See Dep’t of Health, Education, and Welfare, Records,
Computers and the Rights of Citizens 57–58, 61–62
(1973). e HEW committee acknowledged that not all
of the FIPPS would apply to criminal intelligence records,
but insisted that some of the principles must apply: “We
realize that if all of the safeguard requirements were applied
to all types of intelligence records, the utility of many
intelligence-type records . . . might be greatly weakened
. . . It does not follow, however, that there is no need for
safeguards . . . e risk of abuse of intelligence records is
too great to permit their use without some safeguards to
protect the personal privacy and due process interests of
individuals.” Id. at 74–75.
47. See 42 U.S.C. § 14132(b).
48. Note that the perceived “risk” of a deployment may vary by
jurisdiction. Many states have passed so-called “stop and
identify” laws that require the subject of a legal police stop
to produce identication, if available, upon request. See,
e.g., Colo. Rev. Stat. § 16-3-103. In those states, a Stop and
Identify face recognition search run against driver’s license
photos—which automates a process that residents are
legally required to comply with—may be less controversial.
49. e composition of the DNA database (NDIS), the
FBI face recognition database (NGI-IPS), and the FBI
face recognition unit’s network of databases (FBI FACE
Services network) are calculated in terms of biometric
samples (i.e. DNA samples or photographs), whereas the
FBI ngerprint database composition is calculated in terms
of individual persons sampled. For DNA, the Criminal or
Forensic category includes all oender proles (convicted
oender, detainee, and legal proles), arrestee proles,
and forensic proles, as of July 2016. For ngerprints, the
criminal category includes ngerprints that are “submitted
as a result of an arrest at the local, state, or Federal level,”
whereas the non-criminal category includes ngerprints
“submitted electronically by local, state, or Federal agencies
for Federal employment, military service, alien registration
and naturalization, and personal identication purposes.”
For the FBI face recognition database, Criminal or Forensic
photos include “photos associated with arrests (i.e. ‘mug
shots’),” whereas Non-Criminal Photos include “photos
of applicants, employees, licensees, and those in positions
of public trust.” Finally, for the FBI face recognition
network, the Criminal or Forensic category includes
photos of individuals detained by U.S. forces abroad and
NGI-IPS criminal photos and Non-Criminal photos
include photos from visa applications, driver's licenses, and
NGI-IPS civil photos. e Unknown category includes 50
million from four states (Michigan, North Dakota, South
Carolina, and Utah) that allow the FACE Services unit to
conduct or request searches of driver’s license, mug shot,
and correctional photos; unfortunately, the GAO did not
disaggregate these state databases into their component
78
The Perpetual Line-Up
parts, preventing us from distinguishing between criminal
and non-criminal photos. FBI DNA Database (NDIS)
(2016): Federal Bureau of Investigation, U.S. Department
of Justice, CODIS - NDIS Statistics, https://www.fbi.gov/
services/laboratory/biometric-analysis/codis/ndis-statistics
(last visited Sept. 22, 2016) (Total: 15,706,103 DNA
proles); FBI Fingerprint Database (IAFIS) (2016): U.S.
Department of Justice, Federal Bureau of Investigation,
Next Generation Identication Monthly Fact Sheet (Aug.
2016) (identifying 71.9 million criminal subjects and 50.6
million civil subjects); Request for Records Disposition
Authority, Fed. Bureau of Investigation, https://www.
archives.gov/records-mgmt/rcs/schedules/departments/
department-of-justice/rg-0065/n1-065-04-005_sf115.
pdf (clarifying denitions of criminal and civil ngerprint
les); FBI Face Recognition Database (NGI-IPS) (2016):
U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 46, Table 3 (May 2016) (Criminal photos:
24.9 million; Civil photos: 4.8 million); Federal Bureau of
Investigation, U.S. Department of Justice, Privacy Impact
Assessment for the Next Generation (NGI) Interstate Photo
System, https://www.fbi.gov/services/records-management/
foipa/privacy-impact-assessments/interstate-photo-system
(September 2015) (clarifying composition of the Criminal
Identity Group and the Civil Identity Group); FBI Face
Recognition Network (FACE Services) (2016): U.S. Gov’t
Accountability Oce, GAO-16-267, Face Recognition
Technology: FBI Should Better Ensure Privacy and
Accuracy 47-48, Table 4 (May 2016) (Criminal photos 31.6
million; Civil photos: 330.3 million; Unknown photos: 50
million). Note that the IAFIS system has been integrated
into the new Next Generation Identication system. See
Federal Bureau of Investigation, U.S. Department of Justice,
Next Generation Identication, https://www.fbi.gov/services/
cjis/ngerprints-and-other-biometrics/ngi (last visited Sept.
22, 2016). However, we have been unable to nd statistics
on the composition of the ngerprint component of NGI.
50. See, e.g., Michelle Alexander, e New Jim Crow: Mass
Incarceration In e Age Of Colorblindness 132–133 (2010).
51. See Findings: Accuracy.
52. Interview with facial recognition company ( June 22,
2016) (notes on le with authors); Interview with Anil
Jain, University Distinguished Professor, Michigan State
University (May 27, 2016) (notes on le with authors).
53. See ACLU of Illinois, Chicago’s Video Surveillance Cameras:
A Pervasive and Unregulated reat to Our Privacy,
1 (Feb. 2011), http://www.aclu-il.org/wp-content/
uploads/2012/06/Surveillance-Camera-Report1.pdf.
54. Modern state geolocation privacy laws tend to regulate
the acquisition of information from electronic devices, e.g.
mobile phones and wireless carriers. ey do not, on their
face, regulate face recognition, which does not require access
to any device or company database. See, e.g., Utah Code
Ann. § 77-23c-102(1)(a)-(b); Cal. Penal Code § 1546.1(a).
55. We have additionally created a state backgrounder for a
26th jurisdiction, Vermont, detailing that the Vermont
DMV face recognition system may conict with state law.
56. Federal Bureau of Investigation, U.S. Department of Justice,
Long-Time Fugitive Captured: Juggler Was on the Run
for 14 Years (Aug. 12, 2014), https://www.fbi.gov/news/
stories/2014/august/long-time-fugitive-neil-stammer-
captured/.
57. West Valley Community Police Station, Surveillance
Cameras in West San Fernando Valley, West Valley Police
( Jan. 1, 2013), http://www.westvalleypolice.org/index_
news_20130120.html.
58. Darwin Bond-Graham and Ali Winston, Forget the NSA,
the LAPD Spies on Millions of Innocent Folks, LA Weekly
(Feb. 27, 2014), http://www.laweekly.com/news/forget-the-
nsa-the-lapd-spies-on-millions-of-innocent-folks-4473467.
59. See, e.g., Long-Time Massachusetts Fugitive Arrested in North
Carolina, Federal Bureau of Investigation, U.S. Department
of Justice ( June 16, 2016), https://www.fbi.gov/boston/
press-releases/2016/long-time-massachusetts-fugitive-
arrested-in-north-carolina (While this press release does
not mention face recognition, a spokesperson for the
Pinellas County Sheri ’s Oce stated that the suspect’s
identity was conrmed through the use of PCSO’s face
recognition program. Pinellas County Sheri's Oce,
Email from Jake Ruberto, Technical Support Specialist to Clare
Garvie ( Jul. 13, 2016) (on le with authors)); Edward B.
Colby, James Robert Jones, Military Fugitive on the Run Since
1977, Arrested in South Florida: Authorities, NBC Miami
(Mar. 17, 2014), http://www.nbcmiami.com/news/local/
James-Robert-Jones-Military-Fugitive-on-the-Run-Since-
1977-Arrested-in-South-Florida-Authorities-250247711.
html.
60. e U.S. Department of Justice Bureau of Justice Statistics
reports that as of 2013, there were 15,388 state and local
law enforcement agencies. Brian A. Reaves, Ph.D., Local
Police Departments, 2013: Personnel, Policies, and Practices,
Bureau of Justice Statistics, Oce of Justice Programs, U.S.
Department of Justice (May 2015), http://www.bjs.gov/
content/pub/pdf/lpd13ppp.pdf. Based on the responses
to our survey, we estimate that 3,947 state and local law
enforcement agencies (25.6%): (1) currently have the ability
to run or request face recognition searches of their own
system or that of another agency, or (2) have the option
to use face recognition capabilities after requesting access,
fullling training, or signing an agreement with an agency
that has a face recognition system.
61. See Pinellas County Sheri ’s Oce, Florida’s Facial
Recognition Network (Mar. 26, 2014), Document p.
014722. Other examples include the Los Angeles Police
Department, which piloted a face recognition surveillance
camera project by 2005. See LAPD Uses New Technologies
to Fight Crime, Los Angeles Police Department (Feb.
1, 2005), http://www.lapdonline.org/february_2005/
news_view/19849; See Maricopa County Sheri ’s Oce,
Computer Server Purchase for Facial Recognition System (Aug.
28, 2006), Document p. 015026 (indicating that a face
recognition program was "being initiated at the Arizona
Counterterrorism Information Center (ACTIC)" in
conjunction with Hummingbird Defense Systems as early
as 2006).
62. Pinellas County Sheri ’s Oce, Florida's Facial Recognition
Network, FACES Training 2015, Document p. 014396;
Pinellas County Sheri ’s Oce, Interview with PCSO
Sheri Bob Gualtieri and Technical Support Specialist Jake
Ruberto (July 26, 2016) (notes on le with authors)
79
(indicating that 242 agencies at the federal, state, and local
level have access. Notes on le with authors.).
63. Pennsylvania JNET, JNET & PennDOT Facial Recognition
Integration (Dec. 2012), Document p. 013785; Legislative
Budget and Finance Committee, Pennsylvania General
Assembly, Police Consolidation in Pennsylvania (Sept.
2014), http://lbfc.legis.state.pa.us/Resources/Documents/
Reports/497.pdf. Similarly, “any ocer, deputy, investigator
or crime analyst in LA County” is permitted to access the
Los Angeles County Sheri Department’s system. Los
Angeles County Sheri ’s Department, Facial Identication
and the LA Photo Manager (July 23, 2015), Document p.
000532. Participation in the West Virginia Intelligence
Fusion Center “is open to all federal, state, county, and
local agencies.” West Virginia Intelligence Fusion Center,
Standard Operating Procedures, Document p. 009944.
64. U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 51 (May 2016).
65. Pinellas County Sheri ’s Oce, Interview with Sheri Bob
Gualtieri and Technical Support Specialist Jake Ruberto ( July
26, 2016) (notes on le with authors).
66. Maryland Department of Public Safety and Correctional
Services, PIA Request (Feb. 2016), Document pp. 008906–
008907 (describing that both "internal" users—DPSCS
employees, and "external" users—other "law enforcement
ocers or vetted employees of criminal justice agencies"
have access to the face recognition system); Pennsylvania
JNET, JNET & PennDOT Facial Recognition Integration
(Dec. 2012), Document pp. 013785–013787 (“With JFRS
deployed on JNET, the system can be made available to any
law enforcement agency in Pennsylvania . . . With JFRS
available through JNET, this enterprise solution is available
at no cost to any municipal, county, state or federal law
enforcement agency in the commonwealth.”).
67. Michigan’s SNAP database is open to searches from the
U.S. Marshals service. Michigan State Police, Email from
Robert Watson to MSPSNAP (Aug. 17, 2015), Document
p. 011113. e Arizona Counterterrorism Information
Center, run by the Maricopa County Sheri ’s Oce,
has conducted face recognition searches for federal
investigations since 2008. Maricopa County Sheri ’s Oce,
Letter from Deputy Chief Ray Churay to Deputy Chief David
Hendershott (Apr. 21, 2008), Document p. 015070. e
Pinellas County Sheri ’s Oce has signed MOUs with
the U.S. Air Force Oce of Special Investigations, the IRS
Criminal Investigation Field Oce in Tampa, the Social
Security Administration, and four other federal agencies
with Florida branches. Memoranda of Understanding
between Pinellas County Sheri ’s Oce and U.S. Air Force
Oce of Special Investigations Detachment 340 (Jan. 16,
2015), Document p. 013798; Department of Agriculture and
Consumer Services, Oce of Agricultural Law Enforcement
( Jul. 9, 2014), Document p. 013901; Department of
Financial Services, Division of Insurance Fraud (May 14,
2013), Document p. 013906; Internal Revenue Service,
Criminal Investigation, Tampa Field Oce (Aug. 20, 2013),
Document p. 014125; 6th Security Forces Squadron (Jan 21,
2009), Document p. 014208; Social Security Administration,
Oce of the Inspector General, Oce of Investigations (Dec.
16, 2014), Document p. 014594; U.S. Department of
Veterans Aairs Police ( Jun. 16, 2014), Document p. 014649.
Records indicate that the Department of Defense, the
Drug Enforcement Administration, Immigration and
Customs Enforcement, the U.S. Marshals Service, and
numerous other federal agencies access this system as well.
Pinellas County Sheri ’s Oce, Florida's Facial Recognition
Network, FACES Training 2015, Document p. 014396.
68. From 2011 to 2015, federal judges authorized a total of
6,304 wiretaps. See United States Courts, Wiretap Report
2015, http://www.uscourts.gov/statistics-reports/wiretap-
report-2015 (last updated Dec. 31, 2015); United States
Courts, Wiretap Report 2014, http://www.uscourts.gov/
statistics-reports/wiretap-report-2014 (last updated Dec. 31,
2014); United States Courts, Wiretap Report 2013, http://
www.uscourts.gov/statistics-reports/wiretap-report-2013
(last updated Dec. 31, 2013); United States Courts, Wiretap
Report 2012, http://www.uscourts.gov/statistics-reports/
wiretap-report-2012 (last updated Dec. 31, 2012); United
States Courts, Wiretap Report 2011, http://www.uscourts.
gov/statistics-reports/wiretap-report-2011 (last updated
Dec. 31, 2011).
69. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 49 (May 2016).
70. BCI Facial Recognition Video, YouTube (Mar. 6, 2014),
https://www.youtube.com/watch?v=XjvwJlkpFQI at 3:10;
Letter from Gregory Trout, Chief Counsel, Ohio Bureau of
Criminal Investigation to Clare Garvie (Sept. 23, 2016),
Document p. 016841.
71. SANDAG, Board of Directors Agenda (Feb. 13, 2015),
Document p. 005698 (According to SANDAG estimates
from Feb. 13, 2015: “Since August 2012, more than 17,000
image submittals have resulted in approximately 4,700
potential matches.”)
72. Pinellas County Sheri ’s Oce, Florida's Facial Recognition
Network, FACES Training 2015, Document p. 014396.
73. e FBI face recognition unit (FACE Services) has run
an average of 4,055 searches per month over the past 4.5
years. U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 49 (May 2016).
74. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 49 (May 2016).
75. SANDAG, Automated Regional Justice Information System
(ARJIS) Acceptable Use Police for Facial Recognition (Feb. 13,
2015), Document p. 008449.
76. is is mandated by state law. See Mich. Comp. Laws Ann
§ 28.243. Additionally, members of the Digital Analysis
and Identication Division, who are responsible for running
most of the Michigan’s law enforcement face recognition
searches, manually check the potential “match” candidates
returned to ensure that information only about pending
charges and convictions are disseminated. Interview with
Peter Langenfeld, Program Manager, Digital Analysis and
Identication Section (May 25, 2016) (notes on le with
authors).
77. Pinellas County Sheri ’s Oce, Interview with PCSO
Sheri Bob Gualtieri and Technical Support Specialist Jake
80
The Perpetual Line-Up
Ruberto (July 26, 2016) (notes on le with authors). ere
is currently a ve-month backlog for expungement requests
in the Florida Department of Law Enforcement (as of Sep.
21, 2016). Florida Department of Law Enforcement, Seal
and Expunge Process, http://www.fdle.state..us/cms/Seal-
and-Expunge-Process/Seal-and-Expunge-home.aspx (last
visited Sept. 25, 2016).
78. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure
Privacy and Accuracy 51(map). In 2013, the Washington
Post and the Cincinnati Enquirer identied 26 states where
law enforcement could run or request face recognition
searches of driver’s license and ID photo databases. For
most of these states, we were able to use calls, document
requests, and the 2016 GAO report to determine whether
law enforcement access continued or had been discontinued.
See Craig Timberg and Ellen Nakashima, State photo ID-
databases become troves for police, Washington Post ( June 16,
2013) (state map missing; on le with authors); Chrissie
ompson and Jessie Balmert, WATCHDOG: Ohio database
access rules loosest in U.S., Cincinnati Enquirer (Sept. 22,
2013).
79. See Federal Highway Administration, U.S. Department of
Transportation, Highway Statistics 2014 4-5 (Sept. 2015),
http://www.fhwa.dot.gov/policyinformation/statistics/2014/
pdf/dl22.pdf.
80. See above U.S. Census Bureau, Annual Estimates of
the Resident Population for Selected Age Groups by Sex
for the United States, States, Counties, and Puerto Rico
Commonwealth and Municipios: April 1, 2010 to July 1, 2014:
2014 Population Estimates, http://factnder.census.gov/
bkmk/table/1.0/en/PEP/2014/PEPAGESEX.
81. See above note 78.
82. See Federal Highway Administration, U.S. Department of
Transportation, Highway Statistics 2014 4-5 (Sept. 2015),
http://www.fhwa.dot.gov/policyinformation/statistics/2014/
pdf/dl22.pdf.
83. ese are 2014 statistics. See U.S. Census Bureau, Annual
Estimates of the Resident Population for Selected Age Groups
by Sex for the United States, States, Counties, and Puerto Rico
Commonwealth and Municipios: April 1, 2010 to July 1, 2014:
2014 Population Estimates, http://factnder.census.gov/
bkmk/table/1.0/en/PEP/2014/PEPAGESEX.
84. States or agencies are identied in italics if they were
reported to allow or have access to these searches, but we
were unable to verify their current access status.
85. In 2014, the Police Executive Research Forum reported
that New Mexico allowed police to run face recognition
searches on the state’s driver’s license database. Police
Executive Research Forum, Future Trends in Policing (2014),
http://www.policeforum.org/assets/docs/Free_Online_
Documents/Leadership/future%20trends%20in%20
policing%202014.pdf.
86. Daniil Turovsky, e end of privacy: ‘Meduza’ takes a
hard look at FindFace and the looming prospect of total
surveillance, Medusa ( July 14, 2016), https://meduza.io/en/
feature/2016/07/14/the-end-of-privacy.
87. Tygart Technology, MXSERVER™ Overview, www.tygart.
com/products/mxserver (last visited Sept. 22, 2016). In its
“Statement of Need” and sole source purchase justication
for face recognition, the West Virginia Intelligence Fusion
Center listed real-time capabilities as one of the system’s
minimum requirements. West Virginia Intelligence Fusion
Center, WV Intelligence Fusion Center Statement of Need,
Document pp. 009971–009973.
88. Chicago Police Department, FY09 Transit Security Grant
Program: CTA’s Regional Transit Terrorism Prevention and
Response System (T-CLEAR) (Sept. 12, 2012), Document p.
008725. e grant narrative also detailed the Department’s
plan to purchase video processing software that “will
eciently and in real-time compare the face [captured
through a video stream] to the set of faces in the data
structure.” Chicago Police Department, FY09 Transit
Security Grant Program: CTA’s Regional Transit Terrorism
Prevention and Response System (T-CLEAR) (Sept. 12,
2012), Document p. 008726.
89. Law Enforcement Support Agency (South Sound 911),
Mug shot Booking Photo Capture Solution: Section II Project
Background, Document p. 012048.
90. Seattle Police Department, Seattle Police Manual: Booking
Photo Comparison Software, Document p. 009907. e
Seattle Police Department adopted the contract between
Dynamic Imaging, the face recognition vendor company,
and South Sound 911, which explains why the initial RFP
was issued by South Sound 911, but the use policy was
drafted by the Seattle Police Department. Seattle Police
Department, City Purchasing Current Contract Information:
Regional Booking Photo Comparison System, Document
p. 011066; Interview with Sean Whitcomb, Seattle Police
Department (Sept. 13, 2016) (notes on le with authors).
91. See Brandon Formby, DART addresses ‘Big Brother’ fears over
facial recognition software, Dallas Morning News (Feb 17,
2016), http://transportationblog.dallasnews.com/2016/02/
dart-eyeing-facial-recognition-software-for-its-buses-
trains-and-stations.html/. e DART General Counsel’s
oce was unable to locate records in response to our request
for information on this system; however, ocials conrmed
in a phone interview that negotiations were underway
to secure funding for the system. Interview with DART
General Counsel’s Oce (notes on le with authors).
92. Patrick Grother and Mei Ngan, Face Recognition Vendor
Test: Performance of Face Identication Algorithms, NIST
Interagency Report 8009 (May 26, 2014), http://biometrics.
nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.pdf (“As
in the 2010 test, the algorithms from the NEC corporation
give broadly the lowest error rates on all datasets”).
93. NeoFace Watch: High performance face recognition, NEC
Global Safety Division (Sept. 2014), http://www.nec.com/
en/global/solutions/safety/face_recognition/PDF/Face_
Recognition_NeoFace_Watch_Brochure.pdf (last visited
Sept. 23, 2016).
94. See Business Wire, 3M Live Face Identication System
Takes Security Solutions from Reactive to Proactive
(Sept. 20, 2016), http://www.businesswire.com/news/
home/20160920006378/en/3M-Live-Face-Identication-
System-Takes-Security.
81
95. FaceVACS-VideoScan, Cognitec Systems (June 2016), http://
www.cognitec.com/les/layout/downloads/FaceVACS-
VideoScan-5-3-yer.pdf (last visited Sept. 23, 2016).
96. Real-time video screening system: Morpho Argus, Safran ( June
3, 2015), http://www.morpho.com/en/video/605 (last
visited Sept. 23, 2016).
97. South Sound 911, Scope of Work, Dynamic Imaging Systems,
Inc., (Feb. 20, 2013), Document p. 009582.
98. FACE Watch Plus Real Time Screening, DataWorks Plus,
http://www.dataworksplus.com/rts.html (last visited Sept.
23, 2016).
99. See Karen Weise, Will a Camera on Every Cop Make
Everyone Safer? Taser inks So, Bloomberg BusinessWeek
( July 12, 2016), http://www.bloomberg.com/news/
articles/2016-07-12/will-a-camera-on-every-cop-make-
everyone-safer-taser-thinks-so.
100. See Kevin Collier, Inside the Government Centers Where the
FBI Shares Intel with the Police, Vocativ (Aug. 8, 2016),
http://www.vocativ.com/347400/fusion-center-cops-fbi-
share-data/.
101. See Face in Video Evaluation (FIVE), National Institute of
Standards and Technology, U.S. Department of Commerce,
http://www.nist.gov/itl/iad/ig/ve.cfm (last visited Sept. 23,
2016).
102. Magisterial District Courts of Pennsylvania are courts of
limited jurisdiction that handle landlord-tenant disputes,
small claims of up to $12,000, summary oenses, municipal
code violations, and preliminary hearings and arraignments
in misdemeanor and felony oenses that will be tried
in higher courts. Magisterial District Courts, County of
Montgomery Magisterial District Courts, Montgomery
County, http://www.montcopa.org/300/Magisterial-
District-Courts (last visited Aug. 18, 2016). Cheltenham
Township is served by Magisterial District Court 38-1-
02 and 38-1-03. Magisterial District Courts, County of
Montgomery Magisterial District Courts, Montgomery
County, (Sept. 19, 2016), http://www.montcopa.org/
DocumentCenter/View/10059 (last visited Aug. 18, 2016).
103. See Pennsylvania JNET, Pennsylvania Justice Network
2012–2013 Annual Report, Document p. 016738; Welcome
to the JNET Facial Recognition System Slides (May 5, 2014)
[emphasis mine] Document p. 010750.
104. See Olmstead v. United States, 277 U.S. 438, 473–75 (1928).
105. See Katz v. United States, 389 U.S. 347, 351 (1967).
106. See Katz v. United States, 389 U.S. 347, 361 (1967) (Harlan,
J., concurring).
107. See United States v. Knotts, 460 U.S. 276, 282 (1983)
(nding that a criminal defendant lacked a reasonable
expectation of privacy in his movements on public roads
where those movements were visually observable to the
public, and that police use of a beeper to track those
movements “does not alter the situation”).
108. 132 S. Ct. 945 at 953 (2012).
109. See United States v. Jones ,132 S. Ct. 945 at 954 (Sotomayor,
J., concurring); 132 S. Ct. 945 at 957–58 (Alito, J.,
concurring) (2012).
110. See Riley v. California, 134 S.Ct. 2473, 2488 (2014).
111. See Maryland v. King, 133 S.Ct. 1958, 1968–1969 (2013)
(“It can be agreed that using a buccal swab on the inner
tissues of a person’s cheek in order to obtain DNA samples
is a search.”); Florida v. Hayes, 470 U.S. 811, 816 (1985)
(nding that a Fourth Amendment seizure has clearly
occurred where the police “forcibly remove a person from
his home or other place in which he is entitled to be and
transport him to the police station”).
112. Pinellas County Sheri ’s Oce, Standard Operating
Procedure POB 52: Mobile Biometric Usage ( Jan. 12, 2016),
Document p. 014375.
113. Pinellas County Sheri ’s Oce, Mobile Biometric Usage
Policy (Apr. 26, 2016), Document p. 014375.
114. Cell-site location tracking—tracking a suspect’s smartphone
by getting location information from his wireless carrier—
hits another Fourth Amendment hurdle: e idea that we
have no reasonable expectation of privacy in information we
volunteer to a third party—in the present case, our phone
company. See, e.g., Smith v. Maryland, 442 U.S. 735, 746–47
(1979); United States v. Graham, 824 F.3d 421, 437-38 (4th
Cir. 2016) (en banc).
115. See In re Application for Tel. Info. Needed for a Criminal
Investigation, 119 F. Supp. 3d 1011, 1023 (N.D. Cal.
2015), appeal dismissed (Feb. 5, 2016) (nding that
“individuals have an expectation of privacy in the historical
CSLI associated with their cell phones, and that such an
expectation is one that society is willing to recognize as
reasonable”); id at 1039 (requiring a warrant for historical
cell-site location information); In re U.S. for an Order
Authorizing the Release of Historical Cell-Site Info., 809 F.
Supp. 2d 113, 119–20 (E.D.N.Y. 2011) (nding that “cell-
phone users maintain a reasonable expectation of privacy in
long-term cell-site-location records”); id at 127 (requiring a
search warrant for historical cell-site location information).
116. See Commonwealth v. Augustine, 4 N.E.3d 846, 865-66
(Mass., 2014), State v. Earls, 70 A.3d 630, 644 (N.J., 2013).
Separately, see Tracey v. State, 152 So. 3d 504, 526 (Fla.
2014), reh'g denied (Dec. 8, 2014) (imposing a probable
cause requirement for obtaining real-time cell-site location
data in Florida);
117. See e.g., In re Application for Tel. Info. Needed for a
Criminal Investigation, 119 F. Supp. 3d 1011, 1020–21
(discussing the Knotts court’s indication that dragnet-style
surveillance would raise issues distinct from those presented
in the instant case); Tracey v. State, 152 So. 3d at 513
(discussing the Knotts court’s indication that dragnet-style
surveillance would raise issues distinct from those presented
in the instant case).
118. See generally Cal. Penal Code §§ 1546 et seq.; Colo. Rev.
Stat. Ann. § 16-3-303.5; 724 Ill. Comp. Stat. Ann. 168/1 et
seq.; Ind. Code Ann. §§ 35-33-5-15; Me. Rev. Stat. tit. 16,
§§ 647 et seq.; Md. Code Ann. Crim. Proc. §1-203.1; Minn.
Stat. § 626A.42; Mont. Code Ann. § 46-5-110; N.H. Rev.
82
The Perpetual Line-Up
Stat. Ann. §§ 644-A:1 et seq.; R.I. Gen Laws Ann. §§
12-32-1 et seq.; Tenn. Code Ann. § 39-13-610; Utah Code
Ann. §§ 77-23c-101 et seq.; Va. Code Ann. § 19.2-70.3; 13
V.S.A. § 8102; Wash. Rev. Code Ann. §§ 9.73.260 et seq.;
Wis. Stat. Ann. § 968.373; Alaska Stat. Ann. §§ 18.65.900
et seq.; Ind. Code Ann. 35-33-5-9; Fla. Stat. Ann. § 934.50;
Ind. Code Ann. 35-33-5-9; 725 Ill. Comp. Stat. Ann. 167/1
et seq.; Me. Rev. Stat. tit. 25, § 4501; 2015 Nev. Rev. Stat.
Ann. §493.112(2)-(4); N.C. Gen. Stat. Ann. §§ 15A-300.1
et seq.; N.D. Cent. Code Ann. §§ 29-29.4-01 et seq.; Or.
Rev. Stat. §§ 837.310 et seq.; Tenn. Code Ann. §§ 39-13-
609, 39-13-902; Utah Code Ann. §§ 63G-18-101 et seq.;
Vt. Stat. Ann. Tit. 20, § 4622; Va. Code Ann. § 19.2-60.1.
119. Cal. Veh. Code § 2413; Cal. Civ. Code §§ 1798.29,
1798.90.5; Colo. Rev. Stat. Ann. § 24-72-113; Me. Rev.
Stat. tit. 29-a, § 2117-A; Md. Code Ann., Pub. Safety §
3-509; Minn. Stat. §§ 13.82, 13.824, 626.847; N.H. Rev.
Stat. Ann. §§ 261.75-b, 236.130; N.C. Gen. Stat. Ann. §§
20-183.30 et seq.; Utah Code Ann. §§ 41-6a-2001 et seq.;
Vt. Stat. Ann. tit. 23, §§ 1607 et seq.
120. See Or. Rev. Stat. § 133.741(1)(b)(D).
121. See N.H. Rev. Stat. Ann. § 105-D:2(XII) (eective Jan. 1,
2017).
122. e city of Cincinnati and police departments in ve other
localities bar searches of body-worn camera recordings,
but allow analysis of footage from particular incidents. See
Cincinnati Police Department, Body Worn Camera System
( July 14, 2016), https://www.bwcscorecard.org/static/
policies/2016-07-14 Cincinnati - BWC Policy.pdf (“Stored
video and audio from a BWC shall not . . . Be searched
using facial recognition software. [ . . . ] is does not
prohibit CPD from using recognition software to analyze
the recording of a particular incident when reasonable
suspicion exists that a specic suspect or person in need
of assistance may be a subject of a particular recording.”);
Baltimore Police Department, Policy 824: Body Worn
Cameras Pilot Program (Oct. 26, 2015), https://www.
bwcscorecard.org/static/policies/2015-10-26%20
Baltimore%20-%20BWC%20Policy.pdf ( “Stored video
and audio data from a BWC shall not … be searched
using facial recognition software” but same exception);
Baltimore County Police Department, BCoPD Body-
Worn Camera Use Policy at “System Recordings”, https://
www.bwcscorecard.org/static/policies/2016-07-14%20
Baltimore%20County%20-%20BWC%20Policy.pdf
(“System records … may not be … searched using facial
recognition software” but same exception); Montgomery
County Police Department, Body Worn Camera System
(Apr. 20, 2016), https://www.bja.gov/bwc/pdfs/MCPD-
BWCS-Pilot-Program-Summary-Report.pdf ( “e stored
video and audio data from a BWCS recording may not …
be searched using facial or voice recognition software” but
same exception); Parker Police Department, Parker Police
Department Policy and Procedures Manual: Recording Devices
and Imaging Equipment (May 6, 2016),, https://www.
bwcscorecard.org/static/policies/2016-05-06 Parker - BWC
Policy.pdf (“e Department shall not utilize any biometric
technology, such as facial recognition, to conduct searches
of video les. Stored video and audio data from a BWC
shall not … be searched using facial recognition software”
with same exception). e Boston police adopted a policy
that appears to bar real-time face recognition. See Boston
Police Department, Body-Worn Camera Pilot Program Policy
( July 12, 2016), https://www.bwcscorecard.org/static/
policies/2016-07-12 Boston - BWC Policy.pdf (“BWC’s
will not include technological enhancements including,
but not limited to, facial recognition or night-vision
capabilities.”).
123. Vt. Stat. Ann tit. 20 § 4622(d)(2) (“Facial recognition or any
other biometric matching technology shall not be used on
any data that a drone collects on any person, home, or area
other than the target of the surveillance.”).
124. Me. Rev. Stat. Ann. tit. 25 § 4501(5)(D) (“Restrictions on
the use of … facial recognition technology, thermal imaging
and other such enhancement technology”).
125. See Mich. Comp. Laws Ann. § 28.243(7)-(8).
126. Mich. Comp. Laws Ann. § 28.243 at (1), (2), (4), (5); Mich.
Comp. Laws Ann. § 28.248.
127. See Me. Rev. Stat. Ann. tit. 29-A, § 1401 (“9. Use of
biometric technology. e Secretary of State may not use
biometric technology, including, but not limited to, retinal
scanning, facial recognition or ngerprint technology, to
produce a license or nondriver identication card.”); Mo.
Ann. Stat. § 302.189 (“e department of revenue shall
not use, collect, obtain, share, or retain biometric data nor
shall the department use biometric technology, including,
but not limited to, retinal scanning, facial recognition or
ngerprint technology, to produce a driver's license or
nondriver's license or to uniquely identify licensees or
license applicants for whatever purpose.”); N.H. Rev. Stat.
Ann. § 260:10-b (“e state shall not collect, obtain, or
retain any biometric data in connection with motor vehicle
registration or operation, or in connection with driver
licensing.”) and N.H. Rev. Stat. Ann. § 263:40-b (“e
department is prohibited from using any facial recognition
technology in connection with taking or retaining any
photograph or digital image for purposes of this chapter.”);
Vt. Stat. Ann. tit. 23, § 634(c) (“e Department of Motor
Vehicles shall not implement any procedures or processes
for identifying applicants for licenses, learner permits,
or nondriver identication cards that involve the use of
biometric identiers.”)
128. See Wash. Rev. Code Ann. § 46.20.037(1) (stipulating that
DMV may use its face recognition system “only to verify”
applicants identities and prevent identity fraud).
129. See Wash. Rev. Code Ann. § 46.20.037(4)(d); Or. Rev. Stat.
Ann. § 807.026 (“biometric data may not be made available
to anyone other than employees of the [Department of
Transportation] acting in an ocial capacity”).
130. Haw. Code R. § 19-122-1(g) (“Except as may be required
by law, the examiner of drivers shall not permit a digital
image or personal information obtained from a state of
record to be accessed or used by a law enforcement agency
or personnel of such agency for any other purpose.”)
131. See Vt. Stat. Ann. tit. 23, § 634(c).
132. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 47 (May 2016).
133. See Oce of Public Aairs, Department of Justice, Justice
Department Announces Enhanced Policy for Use of Cell-Site
83
Simulators (Sept. 3, 2015), https://www.justice.gov/opa/pr/
justice-department-announces-enhanced-policy-use-cell-
site-simulators.
134. U.S. Department of Justice, Department of Justice Policy
Guidance: Use of Cell-Site Simulator Technology (Sept. 3,
2015) at 4, https://www.justice.gov/opa/le/767321/
download.
135. See Department of Justice, Department of Justice Policy
Guidance: Use of Cell-Site Simulator Technology (Sept. 3,
2015) at 3, https://www.justice.gov/opa/le/767321/
download (“While the Department has, in the past,
appropriately obtained authorization to use a cell-site
simulator by seeking an order pursuant to the Pen Register
Statute [sic], as a matter of policy, law enforcement agencies
must now obtain a search warrant supported by probable
cause…”) (emphasis added); see also 18 U.S.C. § 3122(b)
(2) (pen register statute requiring “a certication… that the
information obtained is relevant to an ongoing criminal
investigation being conducted by that agency”).
136. ese agencies are: Baltimore Police Department; Chicago
Police Department; Daytona Beach Police Department;
Iowa Department of Public Safety; Jacksonville Sheri ’s
Oce; Los Angeles County Sheri ’s Department; Los
Angeles Police Department; Maryland Department of
Public Safety and Correctional Services; Maryland State
Police; Miami Police Department; Minnesota Department
of Public Safety; Montgomery County Police; Nebraska
State Patrol; San Francisco Police Department; Tampa
Police Department; Texas Department of Public Safety;
Virginia State Police; Arizona Department of Public Safety;
Auburn Police Department; Illinois State Police; Kansas
City Police Department; New Bedford Police Department;
Plymouth County Sheri ’s Department; and the San Jose
Police Department.
137. See Daytona Beach Police Department, Interview with
Jimmy Flynt ( Jan. 19, 2016) Document p. 000107;
Jacksonville Sheri ’s Department, Interview with Crime
Analysis Unit Manager Celbrica Tenah (Feb. 17, 2016)
Document p. 010709; Nebraska State Patrol, Letter from
Agency Legal Counsel Wendy Wussow (Feb. 16, 2016),
Document p. 009181; Kansas City Police Department,
Interview with Sgt. Jake Becchina (Jan. 28, 2016), Document
p. 010191; Iowa Department of Public Safety, Letter from
Commissioner Roxann M. Ryan to Clare Garvie (Apr. 1,
2016), Document p. 011911 (“Our Department has not yet
adopted a nal policy.”). Note that while the Daytona Beach
and Jacksonville jurisdictions access the Pinellas County
Sheri ’s Department face recognition system, they do not
appear to be required to follow the Pinellas County use
policy.
138. e Michigan State Police requires probable cause, or
that a subject is unable to provide identication due to
incapacitation, for the use of face recognition on mobile
devices. See Michigan State Police, SNAP Acceptable
Use Policy, Document pp. 011436–011439 (Michigan
Department of State images encompass driver’s license
photographs). It is unclear from the use policy what the
standard is for desktop searches, but in correspondence the
Department indicated that a “law enforcement reason” is
required. Michigan State Police, Letter to Clare Garvie on
state one-page feedback, Document p. 016824. Both mobile
and desktop systems can run searches against Michigan’s
driver’s license photo database. Michigan State Police
therefore is listed both in Probable Cause : High Risk
Deployment and Unknown : High Risk Deployment.
139. e other agencies are the Michigan State Police and
the Pennsylvania Justice Network. Federal Bureau of
Investigation, Department of Justice, Privacy Impact
Assessment for the FACE Services Unit, at 10–11 (May 1,
2015), https://www.fbi.gov/services/records-management/
foia/privacy-impact-assessments/facial-analysis-
comparison-and-evaluation-face-services-unit (“Probe
photos are potential subjects, victims, or witnesses of/
to federal crimes that have been collected pursuant to
authorized FBI investigations.”). Michigan State Police,
Interview with Peter Langenfeld, Program Manager,
Digital Analysis and Identication Section (Mar. 23, 2016),
Document pp. 010928 (MSP allows face recognition
searches to identify witnesses to a crime, not just criminal
suspects) Michigan State Police, Mobile Facial Recognition,
Web Application Instructions, Document p. 011345 (e
image of “capture” drop-down includes the categories:
insucient ID, warrant, criminal suspect, witness, victim,
other as categories under which an ocer can add a probe
on a mobile device.). Pennsylvania Justice Network, JNET
Facial Recognition User Guide, Document p. 010845 (“Facial
recognition is used primarily with images of suspects or
witnesses from surveillance or CCTV cameras, but can
also be used with photos from other sources, such as social
media sites or still photos.”)
140. See e Omnibus Crime Control and Safe Streets Act of
1968 (Pub.L. 90–351, 82 Stat. 197, enacted June 19, 1968,
codied at 42 U.S.C. § 3711); 18 U.S.C. § 2516(1)-(2).
141. See Criminal Justice Information Services Division, Federal
Bureau of Investigation, U.S. Department of Justice,
Interstate Photo System (IPS) Policy and Implementation
Guide (Version 1.2) (Sept. 3, 2014) Document p. 009325
(not establishing any crime-based limitation on searches
and stating that “[i]t is the responsibility of the user
agency to develop appropriate usage policies for the IPS
component…”).
142. See Criminal Justice Information Services Division, Federal
Bureau of Investigation, U.S. Department of Justice,
Interstate Photo System (IPS) Policy and Implementation Guide
(Version 1.2) (Sept. 3, 2014) at 3, Document p. 009320)
(specifying that such photos must be “lawfully obtained
pursuant to an authorized criminal investigation and
meeting a felony crimes against persons Uniform Crime
Report coding denition”).
143. Maryland v. King, 133 S.Ct. 1958, 1979-80 (2013); Md.
Code Ann., Pub. Safety 2-504(d)(1).
144. Maryland v. King, 133 S.Ct. 1958, 1977-78 (2013).
145. See Nebraska State Patrol, Memorandum of Understanding
between the Nebraska State Patrol and the Nebraska DMV,
Document p. 009190 (restricting Nebraska State Patrol’s
access to the Nebraska DMV’s photo repository for the
purpose of “enhanc[ing] the ID eft Task Force Working
Relationship between the NSP and the DMV”).
146. See Alvaro Bedoya, e Color of Surveillance, Slate ( Jan. 18,
2016), http://www.slate.com/articles/technology/future_
84
The Perpetual Line-Up
tense/2016/01/what_the_fbi_s_surveillance_of_martin_
luther_king_says_about_modern_spying.html.
147. Richard C. Coman, Eyewitness to J. Edgar Hoover’s FBI
423 (2014).
148. Coman held weeklong photography courses for law
enforcement ocials, multiple times a year, to train them
in remote photographic surveillance of demonstrations.
At those trainings, “[t]o keep the classes awake,” he would
distribute albums of photos he deemed “attention getters”
to “pep up” his class. ese included photos of “sexually
enthusiastic students cavorting nude in the Reecting Pool;”
a “shot of a minor movie starlet smiling[], but panty-less”; a
“‘sneak shot’ of an ‘Oreo-cookie couple’s ‘making-out’; and a
“surreptitious. . . ‘close-up’ shot of a top-lessly attired hippie
teen-age girl.” Richard C. Coman, Eyewitness to J. Edgar
Hoover’s FBI 425-26 (2014).
149. See Federal Bureau of Investigation Director James Comey,
Hard Truths: Law Enforcement and Race, Remarks at
Georgetown University (Feb. 12, 2015), https://www.fbi.
gov/news/speeches/hard-truths-law-enforcement-and-race
(“ere is a reason that I require all new agents and analysts
to study the FBI’s interaction with Dr. Martin Luther King,
Jr., and to visit his memorial in Washington as part of their
training … to ensure that we remember our mistakes and
that we learn from them.”)
150. See United States. Cong. Sen. Subcommittee on Privacy,
Technology of the Law, Sen. Committee on the Judiciary,
What Facial Recognition Technology Means for Privacy and
Civil Liberties, July 18, 2012, 112th Cong. 2nd sess..
151. See Eric Tucker, Comey: FBI used aerial surveillance
above Ferguson, Associated Press (Oct. 22, 2015), http://
www.salon.com/2015/10/22/comey_fbi_used_aerial_
surveillance_above_ferguson/.
152. See George Joseph, Exclusive: Feds Regularly Monitored Black
Lives Matter Since Ferguson, e Intercept ( June 24, 2015),
https://theintercept.com/2015/07/24/documents-show-
department-homeland-security-monitoring-black-lives-
matter-since-ferguson/.
153. See WFTS Webteam, Black Lives Matter protesters arrested at
Florida State Fair, WFTS Tampa Bay (Feb. 8, 2016), http://
www.abcactionnews.com/news/local-news/black-lives-
matter-protesters-arrested-at-state-fair.
154. See Laura K. Donohue, Technological Leap, Statutory Gap,
and Constitutional Abyss: Remote Biometric Identication
Comes of Age, 97 Minn. L. Rev. 407, 543–551 (2012).
155. 357 U.S. 449, 1174 (1958).
156. 357 U.S. at 462.
157. Talley v. California, 362 U.S. 60, 63 (1960).
158. McIntyre v. Ohio Elections Com'n, 514 U.S. 334, 357
(1995).
159. For a more in-depth analysis of case precedent in the eld
of Remote Biometric Identication, see Laura K. Donohue,
Technological Leap, Statutory Gap, and Constitutional Abyss:
Remote Biometric Identication Comes of Age, 97 Minn. L.
Rev. 407, 543–551 (2012).
160. In 1972, the Fourth Circuit in Donohoe v. Duling
considered whether the Richmond Police Department had
infringed on people’s freedom of speech and association
by photographing public demonstrations, meetings, and
vigils. In concluding that Tatum controlled, the court held
that the attendees were not “chilled” by the photographic
surveillance or deterred from participating in future public
gatherings. Donohoe v. Duling, 465 F.2d 196, 202 (4th Cir.
1972). In Philadelphia Yearly Meeting of Religious Society
of Friends v. Tate, the ird Circuit in 1975 considered
whether the Philadelphia Police Department had violated
the First Amendment by attending public meetings,
photographing those in attendance, and compiling and
sharing those photographs and other information on
attendees with other law enforcement agencies and private
entities. e court found that “mere police photographing
and data gathering at public meetings” is “legally
unobjectionable and creates at best a so-called subjective
chill” insucient to form the basis of a First Amendment
claim. Phila. Yearly Meeting of Religious Soc’y of Friends v.
Tate, 519 F.2d 1335, 1137–38 (3d Cir. 1975).
161. Hassan v. City of New York, 804 F.3d 277, 285 (3d Cir.
2015).
162. Hassan v. City of New York, 804 F.3d 277, 292 (3d Cir.
2015).
163. As Professor Donohue points out, in his dissent in
Donohoe Judge Harrison Winter found the idea of the
photographs being used to identify unknown meeting-
goers to be a dierent—and distant—proposition. “I cannot
suppose that every time a picture is taken of an unknown
person it is sent to the FBI in order to determine whether
that person is dangerous.” Donohoe v. Duling, 465 F.2d
196, 206 (4th Cir. 1972) (Winter, dissenting). Yet that is
precisely what advanced face recognition would allow.
164. 5 U.S.C. § 552a(e)(7) (2014).
165. See Privacy Act of 1974; Implementation, 81 Fed. Reg.
27288, 27289 (proposed May 5, 2016) (to be codied at 28
C.F.R. pt. 16).
166. e International Justice and Public Safety Network,
Privacy Impact Assessment: Report for the Utilization of Facial
Recognition Technologies to Identify Subjects in the Field (June
30, 2011), Document pp. 016625–016693, 016648–016649 .
167. e International Justice and Public Safety Network,
Privacy Impact Assessment: Report for the Utilization of Facial
Recognition Technologies to Identify Subjects in the Field (June
30, 2011), Document p. 016632.
168. e International Justice and Public Safety Network,
Privacy Impact Assessment: Report for the Utilization of Facial
Recognition Technologies to Identify Subjects in the Field (June
30, 2011), Document p. 016649.
169. e International Justice and Public Safety Network,
Privacy Impact Assessment: Report for the Utilization of Facial
Recognition Technologies to Identify Subjects in the Field (June
30, 2011), Document p. 016649.
170. e Nlets Facial Recognition Workgroup included ocials
from the FBI, New Jersey State Police, Illinois State
Police, Pinellas County Sheri ’s Oce, Delaware State
Police, SANDAG ARJIS, and the Oregon State Police.
85
e International Justice and Public Safety Network,
Privacy Impact Assessment: Report for the Utilization of Facial
Recognition Technologies to Identify Subjects in the Field (June
30, 2011), Document p. 016627.
171. Other agencies, including the Seattle Police Department,
West Virginia Intelligence Fusion Center, and Pennsylvania
JNET, have general policies or procedural orders that
limit what and how information about a person’s religious,
political, or other aliation may be gathered and retained
by the agency. Seattle Police Department, Email from Karim
Miller to Clare Garvie (Sept. 13, 2016), Document p. 016829
(“Information will be gathered and recorded in a manner
that does not unreasonably infringe upon: individual rights,
liberties, and freedoms guaranteed by the Constitution of
the United States and the State of Washington, including
freedom of speech, press, association, and assembly . . .”);
West Virginia Intelligence Fusion Center, Privacy Policy,
Document p. 009926 ("e WVI/FC will not seek or
retain information about individuals or organizations solely
on the basis of their religious, political, or social views or
activities; their participation in a particular noncriminal
organization or lawful event”); Pennsylvania JNET,
Pennsylvania JNET Privacy Policy, Document p. 016804
(“JNET . . . does not seek and/or retain information about
individuals solely on the basis of their religious, political, or
social views or activities; their participation in a particular
noncriminal organization or lawful event . . . “). e PIA
articulates the importance of expressly prohibiting the use
of face recognition specically on protected activities—face
recognition is an identication tool, and “[t]he potential
harm of identication is that it increases the government’s
power to control individuals through the chilling eects.”
e International Justice and Public Safety Network,
Privacy Impact Assessment: Report for the Utilization of Facial
Recognition Technologies to Identify Subjects in the Field (June
30, 2011), Document p. 016648.
172. Ohio Bureau of Criminal Investigation, To Be Added 2016
Date TBD, Document p. 009218 (note this language was
implemented in 2016 and replaced language that did not
address the issue of the use of face recognition on First
Amendment activities).
173. Criminal Justice Information Services Division, Federal
Bureau of Investigation, U.S. Department of Justice,
Interstate Photo System (IPS) Policy and Implementation Guide
(Version 1.2) (Sept. 3, 2014), Document p. 009325.
174. e following states have access to the FBI face recognition
database (NGI-IPS) and provided us with their use policy:
Hawaii; Maryland; Michigan; and Florida.
175. See Simson Garnkle, Future Tech, Discover, 23.9 (2002):
17–20 (reporting false positive error generated by face
recognition technology in use at the Fresno Yosemite
International Airport), http://simson.net/clips/2002/2002.
Discover.09.FaceID.pdf; Cf. Eric Licthblau, U.S. Will Pay
$2 Million to Lawyer Wrongly Jailed, N.Y. Times (Nov.
30, 2006) (describing the case of Brandon Mayeld, who
was wrongly linked to the 2004 Madrid train bombings
as the result of a faulty ngerprint identication); Oce
of the Inspector General, U.S. Department of Justice, A
Review of the FBI’s Handling of the Brandon Mayeld Case
( Jan. 2006) at 1, https://oig.justice.gov/special/s0601/exec.
pdf (describing process by which automated ngerprint
matching system and FBI human examiner incorrectly
matched Mayeld’s prints to Madrid bomber’s).
176. FaceFirst, Frequently Asked Questions, http://www.facerst.
com/faq (last visited Sept. 1, 2016).
177. See FaceFirst, Frequently Asked Questions, http://www.
facerst.com/faq (last visited Sept. 1, 2016) (archived copy
available at https://web.archive.org/web/20160119232512/
http://www.facerst.com/faq and on le with authors)
(acknowledging 95% gure is drawn from a 2006 accuracy
test).
178. See Patrick Grother and Mei Ngan, Face Recognition Vendor
Test: Performance of Face Identication Algorithms, NIST
Interagency Report 8009 (May 26, 2014), http://biometrics.
nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.pdf.
179. SANDAG, ARJIS Contract with Facerst, LLC, Document
p. 008358.
180. See, e.g., Patrick J. Grother, et. al., Multiple-Biometric
Evaluation, Report on the Evaluation of 2D Stll-Image Face
Recognition Algorithms, NIST Interagency Report 7709 at
2, National Institute of Standards and Technology (Aug.
24, 2011), http://ws680.nist.gov/publication/get_pdf.
cfm?pub_id=905968 (“Face images have been collected
in law enforcement for more than a century, but their
value for automated identication remains secondary to
ngerprints.”).
181. See Anil Jain & Brendan Klare, Face Matching and Retrieval
in Forensics Applications, 19 IEEE MultiMedia 1, 20 (“e
face recognition community has recognized four key factors
that signicantly compromise recognition accuracy: pose,
illumination, expression, and aging.”).
182. See Mark Hawthorne, Fingerprints: Analysis and
Understanding 21 (2008) (“Friction skin is permanent. at
is, the skin does not change under normal conditions from
the time of formation until decomposition after death. . .
Friction skin will deteriorate with age as well as all skin,
but classication and identication normally will not be
aected.”).
183. See Turk & Pentland, Eigenfaces for Recognition, 3 J.
Cognitive Neurosci. 1, 71 (1991).
184. See Patrick Grother & Mei Ngan, Face Recognition Vendor
Test: Performance of Face Identication Algorithms, NIST
Interagency Report 8009, 25 (May 26, 2014), http://
biometrics.nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.
pdf.
185. See National Institute of Standards and Technology, U.S.
Department of Commerce, American National Standard
for Information Systems: Data Format for the Interchange of
Fingerprint, Facial & Other Biometric Information, ANSI/
NIST-ITL 1-2011 (Dec. 2013), http://biometrics.nist.gov/
cs_links/standard/ansi_2012/Update-Final_Approved_
Version.pdf.
186. See Federal Criminal Police Oce of Germany, Federal
Ministry of the Interior, Face recognition as a search
tool—foto-fahndung, https://www.bka.de/SharedDocs/
Downloads/EN/Research/PhotographBasedSearches/
fotofahndungAbschlussberichtEnglisch.
df;jsessionid=8A41E1E76C3A9180114D9669DE618B34.
live0612?__blob=publicationFile&v=1 (English version).
86
The Perpetual Line-Up
187. See Federal Criminal Police Oce of Germany, Federal
Ministry of the Interior, Face recognition as a search tool—
foto-fahndung at 6.
188. See Federal Criminal Police Oce of Germany, Federal
Ministry of the Interior, Face recognition as a search tool—
foto-fahndung at 25.
189. Patrick J. Grother, et. al., Multiple-Biometric Evaluation,
Report on the Evaluation of 2D Stll-Image Face Recognition
Algorithms, NIST Interagency Report 7709, 2, National
Institute of Standards and Technology (Aug. 24, 2011),
http://ws680.nist.gov/publication/get_pdf.cfm?pub_
id=905968; Patrick Grother & Mei Ngan, Face Recognition
Vendor Test: Performance of Face Identication Algorithms,
NIST Interagency Report 8009, 58 (May 26, 2014), http://
biometrics.nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.
pdf.
190. See generally Lacey Best-Rowden & Anil Jain, A
Longitudinal Study of Automatic Face Recognition, Proc.
of the IEEE International Conference on Biometrics
(May 19-22, 2015), http://www.cse.msu.edu/rgroups/
biometrics/Publications/Face/BestRowdenJain_
LongitudinalStudyFaceRecognition_ICB15.pdf.
191. See MBE 2010 at Patrick J. Grother, et. al., Multiple-
Biometric Evaluation, Report on the Evaluation of 2D
Still-Image Face Recognition Algorithms, NIST Interagency
Report 7709 at 15, National Institute of Standards and
Technology (Aug. 24, 2011), http://ws680.nist.gov/
publication/get_pdf.cfm?pub_id=905968.
192. U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 14 (May 2016).
193. e nine agencies that provided contract documents,
including RFPs, responses to RFPs, sole source purchasing
documents, and contracts, are: Maricopa County Sheri ’s
Oce; Los Angeles County Sheri ’s Department;
SANDAG; San Francisco Police Department; Pinellas
County Sheri ’s Oce; Michigan State Police; Virginia
State Police; South Sound 911; and the West Virginia
Intelligence Fusion Center (WVI/FC). Agencies with sole
source contracts, either for the initial system purchase or for
the latest system upgrade are: Maricopa County Sheri ’s
Oce; Pinellas County Sheri ’s Oce; Virginia State
Police; and WVI/FC.
194. See Los Angeles County Sheri ’s Oce, Bulletin Number
1: Questions and Responses Release, Multi-Biometric
Identication System (MBIS) Request for Information Number
414-SH (Feb. 2, 2010), Document p. 000205.
195. Both agencies required specic accuracy rates for the
systems’ ngerprint algorithms, broken down by true
match rates, failure to match rates, and by probe image
type such as mobile searches and latent-to-criminal
comparisons (analogous to the remote biometric
identication application of face recognition). See LA
County Sheri ’s Oce, Request for Proposals for Multimodal
Biometric Identication System (MBIS) Solution ( July 2013),
Document pp. 000935–000937; Ohio Bureau of Criminal
Investigation, Response to Ohio Attorney General’s Oce
Request for Proposals No. RFP-BCI-ITS-AB01 from 3M
Cogent, Document p. 016396.
196. See San Francisco Police Department, SFPD Request for
Proposal, Automated Biometric Identication System Section
02—Technical Specications (Mar. 31, 2009), Document pp.
005555–005558.
197. Law Enforcement Support Agency (South Sound 911),
Request for Proposal: Mug Shot Booking Capture Solution,
Specication No. 3002-12-05 (2012), Document p. 009432.
198. See Interview with Face Recognition Company Engineer
(Anonymous) (Mar. 9, 2016) (notes on le with authors).
199. See Ritchie, et al., Viewers base estimates of face matching
accuracy on their own familiarity: Explaining the photo-ID
paradox, 141 Cognition 161–169 (2015).
200. See Christian Meissner & John Brigham, irty Years of
Investigating the Own-race Bias in Memory for Faces: A meta-
analytic review, 7 Psychology, Public Policy, and Law 3–35
(2001).
201. See White, et al., Error Rates in Users of Automatic Face
Recognition Software, PLoS ONE 10(10) (2015). e
study presented the images to subjects for only 18 seconds,
however, so it is possible that results might have improved
if the subjects had more time. Documents from the Facial
Identication Scientic Working Group suggest that
“review” should take 45 sections and “examination” longer
than two hours. See Facial Identication Scientic Working
Group, Guidelines for Facial Comparison Models, Version
1.0 (Feb. 2, 2012), https://www.swg.org/document/
viewDocument?id=25.
202. FBI Face Services, U.S. Gov’t Accountability Oce, GAO-
16-267, Face Recognition Technology: FBI Should Better
Ensure Privacy and Accuracy 17 (May 2016) (searches
are manually reviewed and only “the top one or two”
candidates are returned to the FBI agent); Albuquerque
Police Department, Procedural Order – Facial Recognition
Technology, Document p. 009203 ("When trained RTCC
personnel identify a possible match, they will notify the
ocer or case agent and supply them with possible names
and images of known oenders."); Honolulu Police
Department, Policy: Facial Recognition Program (Sept. 14,
2015), Document p. 014705 ("If the facial recognition
system detects a viable candidate, the CAU shall complete
a follow-up report for the assigned detective. e CAU
analyst's follow-up report shall contain the steps taken
to compare the known and unknown photographs and
how the CAU analyst came to his or her conclusion(s).");
Maricopa County Sheri ’s Oce, MCSO/ACTIC Facial
Recognition Procedures: Image Records Request, Document p.
014963–014965 (describing a process where searches are
reviewed and approved by facial recognition supervisors
twice, and results are accompanied by an explanatory
narrative); Michigan State Police, Statewide Network
of Agency Photos (SNAP) Unit: Overview and Workow,
Document p. 011467–11468 (latent (investigate and
identify) searches go through a team of trained examiners
who narrow down candidates to a single match or none
at all, which are peer reviewed by a second examiner to
conrm the result.); Palm Beach County Sheri ’s Oce,
SOPICS Facial Recognition Program Policy, Document p.
008651 (at least two analysts review candidate lists before
the search results are returned to the requestor); Seattle
Police Department, Booking Photo Comparison Software
Manual (Feb. 19, 2014), Document p. 009907 (“Only
87
Department-Trained Photo Personnel Will Use BPCS”);
West Virginia Intelligence Fusion Center, Letter from
omas Kirk, General Counsel for the Oce of the Secretary,
West Virginia Department of Military Aairs and Public
Safety, to Clare Garvie ( Jan. 25, 2016), Document p. 009911
("When an image has been checked against the facial
recognition database and results are shown, a visual check
by the analyst is performed to check the probability of the
match against the target image.").
203. Interview with Peter Langenfeld, Program Manager, Digital
Analysis and Identication Section (May 25, 2016) (notes on
le with authors).
204. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 14 (May 2016) (“e search of NGI-IPS is a
completely automated process…”).
205. See Facial Identication Scientic Working Group,
Guidelines for Facial Comparison Models, Version
1.0 at 5 (Feb. 2, 2012), https://www.swg.org/
ents;jsessionid=6A11990853BB99B8EBA42E6C03883543.
206. Interview with anonymous engineer ( June 22, 2016) (notes
on le with authors).
207. See Patrick J. Grother, et. al., Multiple-Biometric Evaluation
(MBE) 2010, Report on the Evaluation of 2D Still-Image
Face Recognition Algorithms, NIST Interagency Report
7709 at 3, National Institute of Standards and Technology
(Aug. 24, 2011), http://ws680.nist.gov/publication/get_pdf.
cfm?pub_id=905968
208. See University of Massachusetts — Amherst, Labeled Faces
in the Wild, http://vis-www.cs.umass.edu/lfw/ (last visited
Sept. 22, 2016).
209. Pennsylvania JNET, JNET Facial Recognition Presentation
Slides (2014), Document p. 010750; Michigan State Police,
Interview with Peter Langenfeld, Program Manager, Digital
Analysis and Identication Section (May 25, 2016) (notes on
le with authors).
210. See above Section 1: Accuracy remains a work in progress.
Real-time systems and systems with large databases are
especially error-prone.
211. Brendan F. Klare et al., Pushing the Frontiers of
Unconstrained Face Detection and Recognition: IARPA Janus
Benchmark A, 28 IEEE Conference on Computer Vision
and Pattern Recognition 1 ( June 2015) (“ . . . performance
has begun to saturate on LFW, YTW, and other
unconstrained datasets. At the same time, unconstrained
face recognition is hardly considered a solved problem.”).
212. Oce of the Director of National Intelligence, https://
www.iarpa.gov/ (last visited Sept. 22, 2016).
213. Brendan F. Klare et al., Pushing the Frontiers of Unconstrained
Face Detection and Recognition: IARPA Janus Benchmark A,
28 IEEE Conference on Computer Vision and Pattern
Recognition (June 2015); Oce of the Director of National
Intelligence, Janus, https://www.iarpa.gov/index.php/
research-programs/janus/baa?highlight=WyJqYW51cyJd
(last visited Sept. 22, 2016).
214. See, e.g., Gustave A. Feingold, e Inuence of Environment
on Identication of Persons and ings, 5 J. of the Am. Inst. of
Crim. L. & Criminology 39, 50 (May 1914-March 1915)
(“Now it is well known that, other things being equal,
individuals of a given race are distinguishable from each
other in proportion to our familiarity, to our contact with
the race as a whole.”); Luca Vizioli, Guillaume A. Rousselet,
Roberto Caldara, Neural Repetition Suppression to Identity is
Abolished by Other-Race Faces, 107 Proc. of the Nat’l Acad.
of Sci. of the U.S., 20081, 20081 (2010), http://www.pnas.
org/content/107/46/20081.abstract. is problem is known
as the “other-race” eect. Id.
215. See Seattle Police Department, Booking Photo Comparison
System FAQs, Document p. 009377. In 2009, Scott
McCallum then-systems analyst for the Pinellas County
Sheri ’s Oce face recognition system, made the same
claim to the Tampa Bay Times. “[e software] is oblivious
to things like a person’s hairstyle, gender, race or age,
McCallum said.” Kameel Stanley, Face recognition technology
proving eective for Pinellas deputies, Tampa Bay Times, July
17, 2009, http://www.tampabay.com/news/publicsafety/
crime/facial-recognition-technology-proving-eective-for-
pinellas-deputies/1019492.
216. See Brendan F. Klare et al., Face Recognition Performance:
Role of Demographic Information, 7 IEEE Transactions
on Information Forensics and Security 1789, 1797 (2012)
(hereinafter “Klare et al.”).
217. Interview with Face Recognition Company Engineer
(Anonymous) (Mar. 9, 2016) (notes on le with authors);
Interview with Face Recognition Company Engineer
(Anonymous) (Mar. 16, 2016) (notes on le with authors).
218. See, e.g., Brad Heath, Racial Gap in U.S. Arrest Rates:
‘Staggering Disparity’, USA Today, Nov. 19, 2014, http://
www.usatoday.com/story/news/nation/2014/11/18/
ferguson-black-arrest-rates/19043207.
219. See P. Jonathon Phillips et al., An Other-Race Eect for Face
Recognition Algorithms, 8 ACM Transactions on Applied
Perception 14:1, 14:5 (2011).
220. See, e.g., Google Diversity, Our Workforce: Tech, https://www.
google.com/diversity/ (last visited Sept. 22, 2016) (showing
the 2015 tech workforce to be 81% male and 57% white);
Maxine Williams, Facebook Diversity Update: Positive Hiring
Trends Show Progress, Facebook ( July 14, 2016), http://
newsroom.fb.com/news/2016/07/facebook-diversity-
update-positive-hiring-trends-show-progress/ (showing
that the tech workforce is currently 83% male and 48%
white—a plurality).
221. See Klare et al., above note 216, at 1789.
222. As of Feb. 13, 2015, there were approximately 800 registered
users of TACIDS from 28 law enforcement agencies in the
San Diego area. SANDAG, Board of Directors Agenda Item 2
(Feb. 13, 2015), Document p. 005699.
223. See Klare et al., above note 216, at 1797. A few studies have
contradicted this result. G. H. Givens et al., How Features
of the Human Face Aect Recognition: A statistical comparison
of three face recognition algorithm, Computer Vision and
Pattern Recognition (2004) found that African American
and Asian subjects were easier to recognize, but did so
88
The Perpetual Line-Up
using primitive academic algorithms that are a decade
older than those from the 2012 study. ey were trained
and tested on images collected for the FERET dataset
in 1993-96. Patrick J. Grother, et al., Multiple-Biometric
Evaluation (MBE) 2010, Report on the Evaluation of 2D
Still-Image Face Recognition Algorithms, NIST Interagency
Report 7709 at 55-56, National Institute of Standards
and Technology (Aug. 24, 2011), http://ws680.nist.gov/
publication/get_pdf.cfm?pub_id=905968 also found that
“blacks were easier to recognize than whites for 5 of the
6 algorithms” tested in the study, three of which were the
same commercial algorithms as those tested by Klare et al.
However, the MBE 2010 study provides only a single graph
and a paragraph of analysis to support this nding. We rely
on the analysis by Klare et al., which was more systematic,
comprehensive, and thorough in the way it presented its
ndings.
224. See Klare et al., above note 216, at 1797. is nding is also
supported by P. Jonathon Phillips, et al., Face Recognition
Vendor Test 2002: Evaluation Report (Mar. 2003) at 26–28,
http://www.face-rec.org/vendors/FRVT_2002_Evaluation_
Report.pdf and Patrick J. Grother, et al., Multiple-Biometric
Evaluation (MBE) 2010, Report on the Evaluation of 2D
Still-Image Face Recognition Algorithms, NIST Interagency
Report 7709 at 51, National Institute of Standards and
Technology (Aug. 24, 2011), http://ws680.nist.gov/
publication/get_pdf.cfm?pub_id=905968.
225. See Klare et al., Face Recognition Performance: Role
of Demographic Information, 7 IEEE Transactions on
Information Forensics and Security 1790, 1798 (2012).
is nding is also supported by Phillips, et. al., Face
Recognition Vendor Test 2002: Evaluation Report (Mar.
2003) at 29, http://ws680.nist.gov/publication/get_pdf.
cfm?pub_id=50767. is result is contradicted by Patrick
J. Grother, et. al., Multiple-Biometric Evaluation, Report on
the Evaluation of 2D Stll-Image Face Recognition Algorithms,
NIST Interagency Report 7709 at 51-52, National Institute
of Standards and Technology (Aug. 24, 2011), http://ws680.
nist.gov/publication/get_pdf.cfm?pub_id=905968 which
found no prevailing eect.
226. See Klare et al., Face Recognition Performance: Role
of Demographic Information, 7 IEEE Transactions on
Information Forensics and Security 1790, 1800 (2012) (“Face
recognition performance on race/ethnicity…generally
improves when training exclusively on that same cohort.”).
227. See Klare et al., Interview with Face Recognition Performance:
Role of Demographic Information, 7 IEEE Transactions on
Information Forensics and Security 1790, 1797 (2012) (“ese
results strongly suggest that the female cohort is inherently
more dicult to recognize.”)
228. Interview with anonymous company engineer (Mar. 9, 2016)
(“when you have people with very dark skin, you have a
lower dynamic range, which means that it’s much harder to
capture high-quality images. . . is is one reason why the
performance on black subjects has typically been worse”)
(notes on le with authors).
229. See Pennsylvania JNET, JNET Facial Recognition User Guide
Version 1.8 (Dec. 4, 2014), Document p. 010879–010883.
230. See U.S. Census, Quick Facts: Pennsylvania, http://www.
census.gov/quickfacts/table/PST045215/42#headnote-js-b
(last accessed July 24, 2016).
231. See Patrick J. Grother, et. al., Multiple-Biometric Evaluation,
Report on the Evaluation of 2D Stll-Image Face Recognition
Algorithms, NIST Interagency Report 7709, National
Institute of Standards and Technology 55-56 (Aug. 24,
2011), http://ws680.nist.gov/publication/get_pdf.cfm?pub_
id=905968.
232. San Francisco Police Department, Request for Proposal—
Automated Biometric Identication System, Section 02:
Technical Specications (Mar. 31, 2009), Document pp.
005555–005557.
233. Interview with Face Recognition Company Engineer
(Anonymous) (Mar. 9, 2016) (notes on le with authors);
Interview with Face Recognition Company Engineer
(Anonymous) (Mar. 16, 2016) (notes on le with authors.
In order to obtain candid responses, we assured employees
at these companies that their answers would be reported
anonymously. A third company declined to be interviewed
without a non-disclosure agreement that would prohibit
publication of their responses.
234. Flynn et al., Lessons from Collecting a Million Biometric
Samples, University of Notre Dame/National Institute of
Standards and Technology, https://www3.nd.edu/~kwb/
Flynn_Phillips_Bowyer_FG_2015.pdf.
235. See Klare et al., Face Recognition Performance: Role
of Demographic Information, 7 IEEE Transactions on
Information Forensics and Security 1790, 1798.
236. All arrest ratios have been rounded to the nearest whole
number. Arizona Department of Public Safety, Crime in
Arizona (2014), http://www.azdps.gov/about/reports/
docs/crime_in_arizona_report_2014.pdf (11.34% of
adult arrests were of African Americans in Arizona); U.S.
Census Bureau, 2010-2014 American Community Survey
5-Year Estimates Arizona, https://factnder.census.gov/
bkmk/table/1.0/en/ACS/14_5YR/DP05/0400000US04
(African Americans comprised 4.2% of the population
of Arizona); California Department of Justice, Oce of
the Attorney General, CJSC Statistics: Arrests, https://
oag.ca.gov/crime/cjsc/stats/arrests (last visited Sept. 22,
2016) (22.94% of arrests in Los Angeles were of African
Americans); U.S. Census Bureau, 2010-2014 American
Community Survey 5-Year Estimates Los Angeles, https://
factnder.census.gov/bkmk/table/1.0/en/ACS/14_5YR/
DP05/0500000US06037 (African Americans comprised
8.34% of the population of Los Angeles); Minnesota
Department of Public Safety, Uniform Crime Report
(2014), https://dps.mn.gov/divisions/bca/bca-divisions/
mnjis/Documents/2014-MN-Crime-Book.pdf (24.50%
of arrests were of African Americans in Minnesota); U.S.
Census Bureau, 2010-2014 American Community Survey
5-Year Estimates Minnesota, https://factnder.census.gov/
bkmk/table/1.0/en/ACS/14_5YR/DP05/0400000US277
(African Americans comprised 5.4% of the population
of Minnesota); Pennsylvania Uniform Crime Reporting
System, Crime in Pennsylvania: Annual Uniform Crime
Report (2014), http://www.paucrs.pa.gov/UCR/Reporting/
Annual/AnnualFrames.asp?year=2014 (31.8% of arrests
89
in Pennsylvania were of African Americans); U.S. Census
Bureau, 2010-2014 American Community Survey 5-Year
Estimates Pennsylvania, https://factnder.census.gov/
bkmk/table/1.0/en/ACS/14_5YR/DP05/0400000US42
(African Americans comprised 10.4% of the population
of Pennsylvania); California Department of Justice, Oce
of the Attorney General, CJSC Statistics: Arrests, https://
oag.ca.gov/crime/cjsc/stats/arrests (last visited Sept. 22,
2016) (15.12% of those arrested in San Diego County
were of African Americans), U.S. Census Bureau, 2010-
2014 American Community Survey 5-Year Estimates
San Diego County, https://factnder.census.gov/bkmk/
table/1.0/en/ACS/14_5YR/DP05/0500000US06073
(African Americans comprised 5.0% of the population of
San Diego County); Uniform Crime Reporting Section,
Department of State Police, Crime in Virginia (2014),
http://www.vsp.state.va.us/downloads/Crime_in_Virginia/
Crime_in_Virginia_2014.pdf (44.73% of those arrested
in Virginia were African American); U.S. Census Bureau,
2010-2014 American Community Survey 5-Year Estimates
Virginia, https://factnder.census.gov/bkmk/table/1.0/en/
ACS/14_5YR/DP05/0400000US51 (African Americans
comprised 19.3% of the population of Virginia). Michigan
State Police, Michigan Incident Crime Reporting (2014),
http://www.michigan.gov/documents/msp/Annual_
StatewideArrests_493231_7.pdf (33% of arrests were of
African Americans in Michigan); U.S. Census Bureau,
2010-2014 American Community Survey 5-Year Estimates
Michigan, https://factnder.census.gov/bkmk/table/1.0/en/
ACS/14_5YR/DP05/0400000US26 (African Americans
comprised 14.0% of the population of Michigan); Hawaii
Crime Prevention & Justice Assistance Division, Crime in
Hawaii (2014), https://ag.hawaii.gov/cpja/les/2016/07/
Crime-in-Hawaii-2014.pdf (4% of arrests were of African
Americans in Hawaii); U.S. Census Bureau, 2010-
2014 American Community Survey 5-Year Estimates
Hawaii, https://factnder.census.gov/bkmk/table/1.0/en/
ACS/14_5YR/DP05/0400000US15 (African Americans
comprised 1.9% of the population of Hawaii).
237. Minnesota Department of Public Safety, Uniform Crime
Report (2014), https://dps.mn.gov/divisions/bca/bca-
divisions/mnjis/Documents/2014-MN-Crime-Book.
pdf (24.50% of arrests were of African Americans in
Minnesota); U.S. Census Bureau, 2010-2014 American
Community Survey 5-Year Estimates Minnesota, https://
factnder.census.gov/bkmk/table/1.0/en/ACS/14_5YR/
DP05/0400000US277 (African Americans comprised 5.4%
of the population of Minnesota).
238. Michael Males, San Francisco’s Disproportionate Arrest of
African American Women Persists (Apr. 2015) at 1, http://
www.cjcj.org/uploads/cjcj/documents/disproportionate_
arrests_in_san_francisco.pdf.
239. Interview with Victor Manuel Torres, San Diego Criminal
Attorney (Mar. 8, 2016) (notes on le with authors).
240. SANDAG, SANDAG Public Safety Committee Agenda (Dec.
12, 2014), Document p. 008309.
241. Chrissie ompson, Ohio residents not told how license
photos used, Cincinnati Enquirer (Aug. 26, 2013); Chrissie
ompson, A year later, how secure is Ohio’s facial ID
system? Cincinnati Enquirer (Aug. 15, 2014), http://www.
cincinnati.com/story/news/politics/2014/08/14/ohio-facial-
recognition/14090601/.
242. Tony Webster, Hennepin County Sheri circumvents state to
expand facial recognition database, Tonywebster.com ( Jun. 3,
2016), https://tonywebster.com/2016/06/hennepin-sheri-
facial-recognition/.
243. See, e.g., Pei-Sze-Cheng, I-Team: Use of Facial Recognition
Technology Expands as Some Question Whether Rules are
Keeping Up, NBC New York (Jun. 23, 2015), http://
www.nbcnewyork.com/news/local/Facial-Recognition-
NYPD-Technology-Video-Camera-Police-Arrest-
Surveillance-309359581.html; Peter B. Counter,
Government Use of Facial Recognition Deepens in New York,
FindBiometrics ( Jun. 24, 2015), http://ndbiometrics.com/
government-use-of-facial-recognition-new-york-26244/;
New York Post, NYPD uses high-tech facial-recognition
software to nab barbershop shooting suspect (Mar. 16, 2012),
http://nypost.com/2012/03/16/nypd-uses-high-tech-facial-
recognition-software-to-nab-barbershop-shooting-suspect/.
244. New York City Police Department, Letter from Records
Access Ocer Lieutenant Richard Mantellino to Clare Garvie
(Mar. 30, 2016), Document p. 016726 (letter denying New
York State Freedom of Information Law Request # 2016-
PL-337, denial appealed and determination pending as of
September 2016).
245. Chicago Police Department, Letter from Chicago Police
Department Freedom of Information Ocer K. Tierny to Djana
Martin (Sept. 29, 2015), Document p. 008726, 008729, and
008686.
246. Los Angeles Police Department, Letter from Senior
Management Analyst Martin Bland to Clare Garvie (Feb.
25, 2016), Document p. 000102; Phone messages and
conversations between Mary Taylor, Management Analyst,
Discovery Section and Clare Garvie (Feb. 17, Mar. 22, and
April 34, 2016) (notes on le with authors).
247. West Valley Community Police Station, Surveillance
Cameras in West San Fernando Valley, West Valley
Police ( Jan. 1, 2013), http://www.westvalleypolice.org/
index_news_20130120.html; Oce of the Chief of
Police, Los Angeles Police Department, LIII e Beat
10 (Oct. 2007), http://assets.lapdonline.org/assets/pdf/
october_07_beat_9%20-%20OK.pdf (detailing deployment
of prototype Smart Car with face recognition software); Los
Angeles Police Department, LAPD Uses New Technologies
to Fight Crime (Feb. 1, 2005), http://www.lapdonline.
org/february_2005/news_view/19849 (describing face
recognition as “major technological innovation” of Rampart
Division of LAPD, contributing to 19 arrests).
248. See generally Brady v. Maryland, 373 U.S. 83 (1963).
249. Email from Bob Dillinger, Pinellas County Public Defender, to
Clare Garvie (Aug. 8, 2016) (on le with authors).
250. Interview with Public Defender Bob Dillinger ( July 27, 2016)
(notes on le with authors).
251. ARJIS Facial Recognition Acceptable Use Policy, http://
www.arjis.org/SitePages/Policies.aspx (last visited Sept.
22, 2016); Honolulu Police Department, Policy: Facial
Recognition Program, http://honolulupd.org/information/
index.php?page=viewPolicies (last visited Sept. 22,
2016); Michigan State Police, SNAP Acceptable Use
Policy, http://www.michigan.gov/msp/0,4643,7-123-
72297_64747_64749-357133--,00.html (last visited Sept.
90
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23, 2016); Seattle Police Department, Manual: Booking
Photo Comparison Software, http://www.seattle.gov/police-
manual/title-12---department-information-systems/12045-
--booking-photo-comparison-software (last visited Sept.
23, 2016).
252. Upon request and on an annual basis, SANDAG holds
open Public Safety Committee meetings during which
member agencies provide information about the use of
face recognition pursuant to the requirements provided in
SANDAG’s Face Recognition Acceptable Use Policy. See
SANDAG, ARJIS Acceptable Use Policy for Facial Recognition
(Feb. 13, 2015), Document p. 008453.
253. See S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure
Privacy and Accuracy 21–22 (May 2016), See S. Gov’t
Accountability Oce, GAO/AIMD-00-21.3.1, Standards
for Internal Control in the Federal Government 13 (Nov.
1999); 44 U.S.C. § 101; M-03-22, OMB Guidance for
Implementing the Privacy Provisions of the E-Government
Act of 2002 (Sept. 26, 2003); 5 U.S.C. § 552a(e)(4)
(requiring agencies to publish any “establishment or revision
of ” a system of records in the Federal Register).
254. See Center on Privacy & Technology et. al., Comment
on NPRM 81 Fed. Reg. 27288 (July 6, 2016),
https://www.regulations.gov/document?D=DOJ-
OPCL-2016-0008-0114.
255. See 5 U.S.C. § 552a; Implementation, 81 Fed. Reg. 27288,
27829 (proposed May 5, 2016) (to be codied at 28
C.F.R. pt. 16); see also Center on Privacy & Technology
et. al., Comment on NPRM 81 Fed. Reg. 27288 (July 6,
2016), https://www.regulations.gov/document?D=DOJ-
OPCL-2016-0008-0114 (explaining impact of proposed
exemption of FBI’s NGI System from key Privacy Act
accountability provisions).
256. SANDAG, Automated Regional Justice Information System
(ARJIS) Acceptable Use Policy for Facial Recognition, (Feb.
13, 2015), Document p. 008453 (“e Acceptable Use
Policy for Facial Recognition will be brought to the
SANDAG Public Safety Committee and the SANDAG
Board of Directors at least once per year for review and
determination regarding the need for amendments.”);
e SANDAG Board of Directors “is composed of
mayors, councilmembers, and a county supervisor from
each of the region's 19 local governments.” SANDAG,
Board of Directors, http://www.sandag.org/index.
asp?committeeid=31&fuseaction=committees.detail (last
visited Sept. 22, 2016).
257. SANDAG, Board of Directors Agenda, (Feb. 13, 2015)
Document p. 005696 (“Members of the public may speak to
the Board of Directors on any item at the time the Board is
considering the item.”)
258. South Sound 911, Interview with Sta Attorney Peter
Beckwith and Facial Recognition Technology Program
Manager Matt Johnson, (Feb. 9, 2016), Document p. 011899;
Interview with Doug Klunder, ACLU Privacy Counsel (May
6, 2016) (notes on le with authors) Document p. 012666.
e ACLU provided extensive review and feedback over
the course of a year prior to the adoption of the policy.
Note however that it is not clear whether this was a written
requirement; if the system were to be changed or enhanced
in the future, it would not necessarily receive the same
scrutiny.
259. Michigan State Police, Interview with Peter Langenfeld,
Program Manager, Digital Analysis and Identication Section,
(March 23, 2016) Document p. 010928. We reached out
to the ACLU of Michigan for more information on this
process and they could not corroborate this consultation.
260. Maricopa County Sheri ’s Oce, Letter to requestor,
Document p. 014949; Carlsbad Police Department,
Letter to requestor, Document p. 000149; Carlsbad Police
Department, Letter to requestor, Document p. 000149 (note
that SANDAG states that it conducts audits, but also places
the responsibility on agencies accessing face recognition
to conduct their own audits as well. SANDAG, Automated
Regional Justice Information System (ARJIS) Acceptable Use
Policy for Facial Recognition, (Feb. 13, 2015) Document p.
008452 (“Identifying and addressing intentional misconduct
is the responsibility of the individual agency.”); Maryland
DPSCS, Response to requestor, Document p. 008906;
Albuquerque Police Department, Letter to requestor,
Document p. 009204 (“Does not exist” in response to the
request for audits of the FRT system).
261. ese agencies include: FBI FACE Services, PIA for FACE
Services Unit (May 1, 2015), https://www.fbi.gov/services/
records-management/foipa/privacy-impact-assessments/
facial-analysis-comparison-and-evaluation-face-services-
unit; Pinellas County Sheri ’s Oce, Mobile Biometric
Usage, Document p. 014375; Iowa Department of Public
Safety, Letter from Public Information Ocer Alex Murphy
to Clare Garvie, ( Jan. 29, 2016), Document p. 008658;
Michigan State Police, SNAP Acceptable Use Policy,
Document p. 011439; Pennsylvania JNET, End User
Agreement, Document p. 010945; SANDAG, ARJIS Facial
Recognition Acceptable Use Policy, Document p. 008452;
Seattle Police Department, BPCS Manual, Document p.
009909; West Virginia Intelligence Fusion Center, General
WVI/FC Privacy Policy, Document p. 009939.
262. See PCSO, General Order 12-14: Sheri ’s Oce Biometric
Identication Program, Document p. 013982; PCSO,
Standard Operating Procedure POB 52: Mobile Biometric
Usage, Document p. 014375.
263. Palm Beach Sheri ’s Oce, Interview with Fusion Center
Director Scott Nugent (Jan. 15, 2016), Document p. 008649.
264. Interview with Sheri Bob Gualtieri and Technical Support
Specialist Jake Ruberto (July 26, 2016) (notes on le with
authors).
265. Michigan State Police, Various audit records, Document pp.
011107–011145.
266. See Terry v. Ohio, 392 U.S. 1 (1968) (requiring police
ocers to have a reasonable suspicion that a person is
involved in criminal activity prior to an investigatory stop).
267. See Brown v. Texas, 443 U.S. 47, 53 (1979) (nding that a
Texas statute’s application was unconstitutional because it
required individuals to identify themselves to police even
if the police ocer lacked reasonable suspicion); Hiibel v.
Sixth Judicial Dist. of Nev., Humboldt Cty., 542 U.S. 177,
at *5 (2004) (recognizing the “constitutional limitation”
established in Brown, but upholding a Nevada Stop and
91
Identify statute partly because the statute required that a
eld stop “be justied at its inception”).
268. See Ellen Nakashima, FBI wants to exempt its huge
ngerprint and photo database from privacy protections,
Washington Post ( June 1, 2016) (51 percent of all arrests
in the FBI’s face and ngerprint database “lack nal
dispositions, such as whether a person has been convicted or
even charged”).
269. See Mich. Comp. Laws § 28.243(7)-(8); see also above note
76 and accompanying text.
270. Wash. Rev. Code § 46.20.037(3).
271. See Brinegar v. United States, 338 U.S. 160, 175-176 (1949)
(“[P]robable cause exists where ‘the facts and circumstances
within their [the ocers’] knowledge and of which they
had reasonably trustworthy information [are] sucient in
themselves to warrant a man of reasonable caution in the
belief that’ an oense has been or is being committed.”)
(citations omitted).
272. See Findings: Fourth Amendment).
273. See 18 U.S.C. § 2516 (1)-(2) (limiting federal interception
of wire and oral communications to investigations of certain
federal oenses and state wire and oral interceptions to a
narrow category of felonies).
274. See Terry v. Ohio, 392 U.S. 1, 30 (1968) (recognizing a state
interest in ocer safety during police investigative stops).
275. See Maryland v. King, 133 S. Ct. 1958, 1980 (2013)
(identifying “signicant state interests” in identifying
suspect in custody “so that the criminal justice system can
make informed decisions concerning pretrial custody).
276. See Police across US misuse databases to look up celebrities,
romantic partners and others, Associated Press (Sept. 28,
2016), http://www.nola.com/crime/index.ssf/2016/09/
police_across_us_misuse_databa.html.
277. See, e.g., Cal. Penal Code § 1546 et seq.; 725 Ill. Comp. Stat.
act 168/1 et seq.; Md. Code Ann. Crim. Proc. § 1-203.1; see
above notes 115, 116 and accompanying text.
278. See 18 U.S.C. § 2516(1)-(2) (limiting federal interception
of wire and oral communications to investigations of
certain federal oenses); 18 U.S.C. § 2518(1)(c) (requiring
a statement that other investigative techniques have been
attempted, are unlikely to succeed, or are too dangerous);
18 U.S.C. § 2518(5) (capping wiretap authorizations to an
extendable thirty-day period).
279. See 18 U.S.C. § 2518(7) (allowing warrantless wiretaps in
certain emergency situations, if the procedure is followed by
an application within 48 hours after the wiretap has begun).
280. See 18 U.S.C. § 2519.
281. See, e.g., Pinellas County Sheri's Oce, Request for
Proposal: On-line User Training Program (May 27, 2011),
Document p. 014451 ("In 2001, PCSO initiated a law
enforcement facial recognition program from grants
awarded by the U.S. Department of Justice, Oce of
Community Oriented Policing Services (COPS)."); Seattle
Police Department, Mugshot Booking Photo Comparison
DRAFT - Project Documents, Document p. 012489 ("e
Mugshot Booking Photo [face recognition] comparison
project . . . is a result of funding from a Department of
Homeland Security (DHS) grant under the Urban Area
Security Initiative (UASI) . . ."); SANDAG, Proposed FY
2015 Program Budget Amendment: Urban Area Security
Initiative Tactical Identication System Project (Feb. 13,
2015), Document p. 005716 (describing an award of
$99,000 from the DHS Urban Area Security Initiative
to continue maintaining the Tactical Identication
System, which includes face recognition); Chicago Police
Department, CTA's Regional Transit Terrorism Prevention
and Response System (T-CLEAR) (Sept. 12, 2012),
Document p. 008725–008729 (a grant proposal to the DHS
FY09 Transit Security Grant Program outlining a video
security system that includes face recognition).
282. See 18 U.S.C. §§ 2516 (1)–(2).
283. See Figure 9: Legal Standards for Face Recognition Search,
by Jurisdiction.
284. What Facial Recognition Technology Means for Privacy and
Civil Liberties: Hearing before the Subcomm. on Privacy,
Technology & the Law of the S. Comm. on the Judiciary, 112th
Cong., 10–11 (2012) (“One of the things that the MOUs
that we sign with the agencies that are going to access the
system require is an audit process, so the local agencies are
required to audit the use of the system on an annual basis
to detect any type of misuse. And then, in addition to that,
within our FBI CJIS Division we have an audit unit that
goes out and does triennial audits of the same agencies . . .
a double-check on the audits, as well as to be sure that the
audit processes are in place and being done eectively.”).
285. Federal Bureau of Investigation, U.S. Department of
Justice, Privacy Impact Assessment for the Next Generation
Identication (NGI) Interstate Photo System, (Sept. 2015),
https://www.fbi.gov/services/records-management/foipa/
privacy-impact-assessments/interstate-photo-system.
286. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 25–32 (May 2016) (audits “have not yet
assessed the use of face recognition searches of NGI-IPS”).
287. See, e.g. Michigan State Police, MOU between the FBI and
e Michigan State Police for the Interstate Photo System Facial
Recognition Pilot (IPSFRP) (Apr. 8, 2011), Document pp.
011304–011309; Nebraska State Patrol, MOU between the
FBI and Nebraska State Patrol for the IPSFRP (Oct. 2012),
Document pp. 009183–009189.
288. See U.S. Gov’t Accountability Oce, GAO-16-267, Face
Recognition Technology: FBI Should Better Ensure Privacy
and Accuracy 49, 26 (May 2016) (describing test results on
a dataset of 926,000 photos.
289. U.S. Department of Justice, Justice Department Reaches
Settlement in Civil Rights Lawsuit Against Maricopa County,
Arizona, and Maricopa County Sheri, https://www.justice.
gov/opa/pr/justice-department-reaches-settlement-civil-
rights-lawsuit-against-maricopa-county-arizona (last visited
Oct. 2, 2016).
290. Maricopa County Sheri ’s Oce, Purchase of Equipment
to Enhance the MCSO Facial Recognition Unit at the ACTIC
(Aug. 20, 2007), Document p. 015058.
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291. See above note 236.
292. For more information, see the Center’s ling on the
proposed exemptions. Center on Privacy & Technology
et. al., Comment on Proposed Rule to Exempt Next
Generation Identication System from Provisions of the
Privacy Act and the Modied System of Records Notice for
that System ( July 6, 2016), https://www.regulations.gov/
document?D=DOJ-OPCL-2016-0008-0114 (explaining
the impact of the proposed exemptions).
293. See 18 U.S.C. § 2519.
294. See Mich. Comp. Laws Ann. § 28.248 ("Biometric data
obtained under a law or rule for noncriminal identication
purposes may be used for criminal identication purposes
unless prohibited by law or rule."); Tex. Transp. Code §
521.059 ("e [Department of Motor Vehicles] shall use
the image verication system established under this section
... to aid other law enforcement agencies").
295. See Cognitec, FaceVACS Technology: A16 Algorithm
Performance, http://www.cognitec.com/les/layout/
downloads/FaceVACS-algorithm-performance-A16.pdf
(last visited Sept. 22, 2016).
296. See National Institute of Standards and Technology, U.S.
Department of Commerce, Color FERET Database, http://
www.nist.gov/itl/iad/ig/colorferet.cfm (last visited Sept. 22,
2016).
297. See Shanika Gunaratna, e tech that went into catching the
NY, NJ bombing suspect, CBS News (Sept. 19, 2016), http://
www.cbsnews.com/news/tech-that-went-intro-catching-
nj-nj-bomb-suspect/; Anthony M. DeStefano, How bomb
suspect Ahmad Khan Rahami was caught in just 50 hours,
Newsday (Sept. 19, 2016), http://www.newsday.com/news/
new-york/how-bomb-suspect-ahmad-khan-rahami-was-
caught-in-just-50-hours-1.12339972.
298. See Shanika Gunaratna, e tech that went into catching the
NY, NJ bombing suspect, CBS News (Sept. 19, 2016), http://
www.cbsnews.com/news/tech-that-went-intro-catching-
nj-nj-bomb-suspect/ (“It’s still unclear to what extent
ocials used automated facial recognition technology”);
Anthony M. DeStefano, How bomb suspect Ahmad Khan
Rahami was caught in just 50 hours, Newsday (Sept. 19,
2016), http://www.newsday.com/news/new-york/how-
bomb-suspect-ahmad-khan-rahami-was-caught-in-just-50-
hours-1.12339972 (“NYPD investigators tried to use facial
recognition software to identify Rahami, but the images of
him captured on surveillance cameras around the bombing
sites were either too grainy or didn’t show his face at the
proper angle”).
299. As of October 14, 2016, we have submitted an additional 29
records requests to state agencies not surveyed in the rst
round of records requests.
300. Based on: Michigan State Police, Statewide Network of
Agency Photos (SNAP) Acceptable Use Policy, Document p.
011436.
301. Based on: SANDAG, ARJIS Acceptable Use Policy for Facial
Recognition (Feb. 13, 2015), Document p. 008448.
302. Based on: Honolulu Police Department, Policy: Facial
Recognition Program (Sept. 14, 2015), Document p. 014704.
303. Based on: Seattle Police Manual, Booking Photo Comparison
Software (Feb. 19, 2014), Document p. 009907.
304. Based on: Michigan State Police, Interview with Peter
Langenfeld, Program Manager, Digital Analysis and
Identication Section (May 25, 2016) (describing the practice
of MSP to remove individuals not charged or found not
guilty from the face recognition system in accordance with
MCL § 28.243. Notes on le with authors).
305. Based on: SANDAG, ARJIS Acceptable Use Policy for Facial
Recognition (Feb. 13, 2015), Document p. 008450.
306. Based on: Pennsylvania JNET, JNET Facial Recognition User
Guide (Dec. 4, 2014), Document p. 010907.
307. Based on: SANDAG, ARJIS Acceptable Use Policy for Facial
Recognition (Feb. 13, 2015), Document p. 008450.
308. Based on: Albuquerque Police Department, Procedural
Orders: Facial Recognition Technology, Document pp.
009202–009203.
309. Based on: Michigan State Police, Statewide Network of
Agency Photos (SNAP) Acceptable Use Policy, Document p.
011438.
310. is section describing the role of Facial Examiners is based
on: Michigan State Police, Best Practice Guidelines for Facial
Recognition and Facial Comparison (Feb. 2, 2014), Document
pp. 011323–011331.
311. Ohio Bureau of Criminal Investigation, OHLEG Rules
and Regulations, 1.11: Facial Recognition (July 1, 2016),
Document p. 009218.
312. Based on: Albuquerque Police Department, Procedural
Orders: Facial Recognition Technology, Document p. 009203.
313. Based on: San Diego Police Department Procedure ( June 19,
2015), Document p. 013761.
314. Based on: Michigan State Police, Statewide Network of
Agency Photos (SNAP) Acceptable Use Policy, Document p.
011439.
315. Based on: Michigan State Police, Statewide Network of
Agency Photos (SNAP) Acceptable Use Policy, Document p.
011439.
316. Based on: SANDAG, ARJIS Acceptable Use Policy for Facial
Recognition (Feb. 13, 2015), Document p. 008453.
93
A. RECORDS REQUEST SURVEY
OF LAW ENFORCEMENT
AGENCIES.
We submitted an initial 106 public records
requests to state and local law enforcement
agencies across the country.
299
We selected
agencies that met at least one of two criteria:
1. Agencies we could identify as having piloted
or implemented face recognition. We
identied these agencies from news articles,
vendor or agency press releases and annual
reports, or other publicly available sources
discussing the implementation of face
recognition for law enforcement purposes.
2. e 50 largest law enforcement agencies in
the country, by force size.
Each records request asked for any policies,
manuals, or procedure documents the agency
had created or received; audit reports; training
manuals and technical specications; contracting
and nancial documents; and any memoranda of
understanding or other agreements pertaining to
face recognition. In total, we received substantive
responses from 90 agencies, and over 15,000
pages of responsive records. A list of the agencies
we surveyed, grouped by type of response
received, and a template of the records request,
can be found in this section.
Following up on our records request, we
conducted over a dozen phone interviews with
agency ocials about their current or former
use of face recognition. We also conducted site
visits and more extensive in-person interviews
with two agencies, the Michigan State Police
and the Pinellas County Sheri ’s Oce. In the
interest of obtaining candid answers, some of the
interviews with engineers and vendor companies
were conducted on the condition of anonymity.
All City and State Backgrounders were sent in
draft form to the respective agencies in advance
of publication, with an invitation to submit edits
if needed. We have incorporated the relevant
information that was provided to us in response
to these drafts.
B. FACE RECOGNITION
TECHNOLOGY RESEARCH AND
LITERATURE REVIEW.
To complement our records request survey and
to gain an understanding of the state of face
recognition technology today, we conducted
interviews with researchers both in academia and
government who worked on: (1) the application
of face recognition to law enforcement; (2)
issues surrounding biases in accuracy rates
across race; and (3) the role of trained human
review of face recognition results. We also spoke
with technologists and representatives from
two of the leading companies that provide face
recognition algorithms to law enforcement about
their approach to testing and compensating
for accuracy biases. Additionally, we conducted
an in-depth review of the existing technical
literature on face recognition, focusing
particularly on research addressing the presence
X. METHODOLOGY
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The Perpetual Line-Up
of bias in the accuracy rates of face recognition
algorithms.
C. FIFTY-STATE LEGAL SURVEY
OF BIOMETRICS LAWS.
We conducted a fty-state survey of laws that
may govern or inform the use of face recognition
by law enforcement, or, for comparison, state
laws that govern the use of other tracking or
surveillance technology. is survey answered
the following questions:
1. Does the state have any non-ngerprint
biometrics law that would control law
enforcement use of face recognition?
2. Does the state have a law that either allows
or restricts law enforcement use of or access
to photographs from driver’s license records?
3. Does the state have a “stop-and-identify”
law?
4. Has the state passed a law regulating law
enforcement use of geolocation tracking?
5. Has the state passed a law regulating the use
of drones?
6. Has the state passed a law regulating the use
of automated license plate readers (ALPRs)?
D. THE FACE RECOGNITION
SCORECARD.
We evaluated each agency on seven criteria.
We scored all agencies that (1) owned a face
recognition system and provided us with
responsive documents, as well as (2) the agencies
that access the FBI’s face recognition database,
the Next Generation Identication Interstate
Photo System, and (3) the FBI face recognition
unit (FACE Services). ere is overlap between
the rst two categories. Entries are greyed out
where we did not have sucient information to
evaluate the agency on that criterion.
• People in the Database. Who is enrolled in
the face recognition database or network of
databases available to the law enforcement
agency?
Mug shots of individuals arrested,
with enrollment limited based on
the underlying oense, and/or with
mug shots armatively “scrubbed” by
police to eliminate no-charge arrests
or not-guilty verdicts.
Mug shots of individuals arrested,
with no limits or rules to limit which
mug shots are enrolled, or where
mug shots are removed only after the
individual applies for, and is granted,
expungement.
Driver’s license photos in addition to
mug shots of individuals arrested.
• Real-Time Video Surveillance. How has
the agency addressed the risks of real-time or
historical video surveillance?
Written policy (1) prohibiting the
use of face recognition for real-time
video or historical video surveillance,
or (2) that restricts its use only to
life-threatening public emergencies
and requires a warrant.
No written policy addressing real-
time or historical video surveillance,
but agency has armatively stated
that it does not use face recognition
in this manner.
Agency has deployed, purchased,
or indicated a written interest in
95
purchasing face recognition for real-
time or historical video surveillance
but has not developed a written
policy or armatively disclaimed
these practices.
• 4th Amendment. What legal standard does
the agency require prior to a face recognition
search? is is a bifurcated standard. If the
agency uses face recognition on databases
that include only mug shots, earning it
a “green” or “yellow” in the rst column
(People in Database), the rst standard is
used. If the agency uses face recognition on
databases that include driver’s license photos,
earning it a “red” in the rst column, the
second standard is used.
Targeted database—mug shots only.
Reasonable suspicion for the subject
to be searched, and at least one of the
following: (1) searches are limited to
suspects and victims of crimes; and
(2) Investigate and Identify searches
are limited to felonies only.
Reasonable suspicion for the subject
to be searched but the standard has
exceptions or allows for searches for
bystanders or witnesses as well.
No legal standard stated, or a
statement that face recognition may
be used for any “law enforcement” or
“criminal justice” purpose.
Dragnet database—license and ID photos.
(1) Searches are limited to
investigations of serious oenses
and require a warrant or court order
supported by probable cause; or (2)
searches are limited to identity-
related crimes.
Probable cause or searches are limited
to investigations of serious oenses
(for non-identity related crimes).
Anything less than probable cause
(for non-identity related crimes).
• Free Speech. Has the agency considered
and taken steps to limit the use of face
recognition in a way that would pose risks to
free speech, assembly, and association?
Express statement in a face
recognition use policy prohibiting
the use of face recognition to target
or collect information on individuals
on the basis of their race, religion, or
other bases that may stie speech.
(1) A statement in a face recognition
use policy prohibiting the use of
face recognition in violation of state
or federal law, including the First
Amendment; or (2) a statement in
a general operating policy or police
manual prohibiting the targeting
or collection of information on
individuals on the basis of their race,
religion, or other bases that may stie
speech.
No statements outlined in either
section above.
• Accuracy. How has the agency built
safeguards against errors into their face
recognition program?
Agency demonstrates four or ve
criteria listed below.
Agency demonstrates three of the
criteria.
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The Perpetual Line-Up
Agency demonstrates two or fewer of
the criteria.
e criteria are:
• Algorithms have been tested by the
National Institute of Standards and
Technology;
• Contract with vendor company contains
provisions that require face recognition
algorithms to have been tested for
accuracy and to be tested at all future
opportunities;
• Most or all face recognition queries are
validated by trained human examiners
or agencies have a unit or designated
personnel that perform a review and
screening function of the candidate lists
(weighted as two criteria);
• Face recognition results or candidate lists
are treated as investigative leads only.
• Public Transparency. Has the agency
publicly posted its face recognition use
policy, and has it been reviewed or approved
by a legislature or privacy and civil liberties
groups?
Agency has a public face recognition
use policy that has been reviewed
or approved by a legislature and/or
privacy and civil liberties groups.
Agency has a public use policy,
but there is no evidence the policy
received external review or approval.
Agency has not made its use policy
public, or has no use policy.
• Internal Audits. Does the agency monitor
and conduct audits of face recognition use
by its ocers and other accessing agencies?
(Since our records request specically asked
for records pertaining to audits, when an
agency did not provide audit records or
sample audit forms and did not deny this
request, it was assumed that no audits were
conducted.)
Formal audit procedure is in place
and there is evidence that audits are
indeed conducted.
Audit procedure in place but it is
unclear if audits are conducted.
No audit procedure in place and/or
no audits are conducted.
97
1. Albuquerque Police Department
2. Baltimore Police Department
3. Carlisle Borough Police Department
4. Carlsbad Police Department
5. Chicago Police Department
6. Chula Vista Police Department
7. Daytona Beach Police Department
8. Fairfax County Police Department
9. Hawaii Criminal Justice Data Center
10. Honolulu Police Department
11. Iowa Department of Public Safety
12. Jacksonville Sheri ’s Oce
13. King County Sheri ’s Oce
14. Lincoln Police Department
15. Los Angeles County Sheri ’s Department
16. Los Angeles Police Department
17. Maricopa County Sheri ’s Oce
18. Maryland Department of Public Safety and
Correctional Services
19. Maryland State Police
20. Miami Police Department
21. Michigan State Police
22. Minnesota Department of Public Safety
23. Montgomery County Police
24. Nebraska State Patrol
25. Northern Virginia Regional Information
System
26. Ohio Bureau of Criminal Investigation
27. Palm Beach County Sheri ’s Oce
28. Pennsylvania State Police
29. Pennsylvania JNET
30. Philadelphia Police Department
31. Pierce County Sheri ’s Department
32. Pinellas County Sheri ’s Oce
33. Prince George’s County Police Department
34. San Diego Association of Governments
35. San Diego County Sheri ’s Department
36. San Diego Police Department
37. San Francisco Police Department
38. Seattle Police Department
39. Snohomish County Sheri ’s Oce
40. South Sound 911
41. Tampa Police Department
42. Texas Department of Public Safety
43. Virginia State Police
44. West Virginia Intelligence Fusion Center
45. Arizona Department of Public Safety
46. Auburn Police Department
47. Cumberland County Sheri ’s Department
48. Illinois State Police
49. Kansas City Police Department
50. New Bedford Police Department
51. Plymouth County Sheri ’s Department
52. San Jose Police Department
Formerly used or acquired face recognition
E. AGENCIES SURVEYED GROUPED BY RESPONSE
Currently use or have acquired face recognition
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54. Arkansas State Police
55. Atlanta Police Department
56. Austin Police Department
57. Blount County Police Department
58. Boston Police Department
59. Charleston Police Department
60. Charlotte-Mecklenburg Police
61. Cincinnati Police Department
62. City of Ogden Police
63. Columbus Police Department
64. D.C. Metro Police Department
65. Denver Police Department
66. Detroit Police Department
67. Las Vegas Metro Police
68. Louisville Metro Police
69. Memphis Police Department
70. Milwaukee Police Department
71. Nashville Metro Police
72. New Orleans Police Department
73. Oklahoma City Police
74. Pinal County Sheri ’s Oce
75. San Antonio Police Department
76. Tucson Police Department
77. Vermont State Police
78. Dallas Police Department
79. El Paso Police Department
80. Fort Worth Police Department
81. Houston Police Department
82. Nassau County Sheri ’s Oce
83. New Jersey State Police
84. Oakland Police Department
85. Orange County Sheri ’s Department
86. Phoenix Police Department
87. Rhode Island State Police
88. South Carolina Department of Public
Safety
89. Saint Louis Police Department
90. Utah Department of Public Safety
Planned future use of face recognition
53. Dallas Area Rapid Transit Police
No responsive records—agency stated it does
not use face recognition
No responsive records—response did not
indicate whether other not agency uses face
recognition
Complete denial of records request; appeal
pending
91. New York City Police Department
99
92. Baltimore County Police Department
93. Brockton Police Department
94. Broward County Sheri ’s Department
95. Cleveland Police Department
96. Essex County Police Department
97. Indianapolis Metro Police
98. Massachusetts Department of Public
Safety
99. Miami-Dade County Sheri
100. Mississippi Department of Public Safety
101. New Mexico Department of Public Safety
102. Newark Police Department
103. Raleigh Police Department
104. Salt Lake City Police
105. Saint Louis County Police Department
106. Suolk County Police
No response to records request
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F. RECORDS REQUEST TEMPLATE
[Date]
[Agency Address]
Re.: Public Records Request—Facial Recognition Technology
Dear Public Records Ocer:
e Center on Privacy & Technology, a think tank based at the Georgetown University Law Center, is
conducting a survey of law enforcement agencies’ use of facial recognition technology (FRT). is is part of a
project examining the benets and possible risks of FRT in policing.
Pursuant to [State Records Request Law and citation], we request the following records pertaining to FRT.
We intend this request to cover all software, hardware, databases and other technology used in FRT systems.
However, we realize the following list of records is long, and not all records will be relevant or available.
erefore if it would be helpful, we welcome a phone conversation to narrow this request up front.
Records Requested
Please provide copies of the following records:
• Any manuals, policies, procedures and practices the agency follows for using the FRT system or
requesting a FRT search from another party. is request includes, but is not limited to:
• Procedures for using, deleting or retaining probe photos (photos of subjects being identied);
• Sources of probe photos, such as mobile devices, body cameras or surveillance videos;
• Procedures the agency follows after a positive match, such as requiring independent or in-person
verication;
• Permitted uses of the information created from a system match.
• Any manuals, policies, procedures and practices the agency follows for inputting photos and other
information or migrating photo databases into the FRT system. is could be a list of sources for photos
and other information (e.g., mug shot photos, driver’s license records, or prior probe photos).
• Any audits of the FRT system, including but not limited to: audits of the system, misuse reports, and
reports to oversight bodies.
• e legal standard, if any, (e.g., probable cause, court order, relevance, consent) that is required before
using the FRT system.
• Warrant applications for facial recognition searches, or judicial decisions and orders in the agency’s
possession governing the agency’s use of the FRT system or requests to obtain a facial recognition search.
• Purchasing and procurement documents, including but not limited to: purchase orders, RFPs, responses
to RFPs, invoices, and contracts for FRT hardware, software, and services.
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• Any materials for training law enforcement and other personnel on using and maintaining the FRT
system, including training manuals for mobile devices or other FRT hardware.
• Any manuals from the companies providing FRT system components, including but not limited to any
technical specications they have provided.
• Memoranda of Understanding (MOUs) or agreements with other state or local agencies—such as the
Dep’t of Motor Vehicles or a municipal agency—on the use of, or requests to search, their FRT systems.
Records of the requests made, including but not limited to: the number of requests made and the number
granted.
• MOUs or agreements with federal, state or local law enforcement agencies on the use or sharing of FRT
systems, and the results from those systems, including but not limited to: the number of requests made
and the number granted.
is request is made on behalf of a not-for-prot organization whose mission is to advance the eld of
privacy and technology policy and to train law students from around the county in this eld. Because of our
not-for-prot status and the fact that this request is about a matter in the public interest, we request a fee
waiver. If such a waiver is denied, please inform us in advance if the cost will be greater than $50.
According to [State Records Request Law], a custodian of public records shall comply with a request [within
X business days of receipt / timeframe specied in the law]. Please furnish all responsive documents to Clare
Garvie at cag104@law.georgetown.edu or:
Center on Privacy & Technology
McDonough Hall 444
600 New Jersey Ave, NW
Washington DC 20001
If you have any questions or want to discuss narrowing this request, please contact me at
cag104@law.georgetown.edu within the above timeframe. ank you for your prompt attention to this matter.
Sincerely,
Clare Garvie
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is model bill is written for either Congress or a state legislature.
• e federal bill would control all federal and state law enforcement (1) access to all arrest photo databases
and driver’s license and ID photo databases, and (2) use of real-time face recognition.
• e state bill would control (1) state law enforcement access to arrest photo databases, (2) state and federal
law enforcement access to the driver’s license and ID photo databases maintained by that state, and (2)
state law enforcement use of real-time face recognition within the state.
Language specic to state legislation is in blue; federal legislation language is in red. To produce a copy of the
state bill, delete red text and keep blue text; for the federal bill, delete blue text and keep red text.
is bill is written to regulate law enforcement use of face recognition. However, other remotely capturable
biometric technology—such as iris scanning and voice or gait analysis—is rapidly evolving. e Center on
Privacy & Technology would welcome the opportunity to assist community advocates or elected ocials who
wish to craft legislation to regulate remote biometric identication more broadly.
***
IN THE _____________
A
BILL
To regulate law enforcement use of face recognition technology.
Be it enacted by the ______________________________,
Section 1. Short Title.
is Act may be cited as the “Face Recognition Act of 2016.”
Section 2. Denitions. As used in this Act—
(a) “Face recognition” means the automated or semi-automated process by which a person is identied
or attempted to be identied based on the characteristics of his or her face.
(b) “Targeted face recognition” means the use of face recognition to identify or attempt to identify a
specic person as part of a specic criminal investigation.
XI. MODEL FACE RECOGNITION LEGISLATION
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(c) “Continuous face recognition” means the use of face recognition to identify or attempt to identify
groups of persons as part of a criminal investigation or general surveillance, including the use of face
recognition to continuously identify persons whose images are captured or recorded by a surveillance
camera.
(d) “Arrest photo database” means a database populated primarily by booking or arrest photographs or
photographs of persons encountered by investigative or law enforcement ocers.
(e) “[State] identication photo database” means a database populated primarily by photos from driver’s
licenses or identication documents made or issued by or under the authority of [the/a] State, or a
political subdivision of [the/a] State [, or the United States government].
(f ) “Emergency watchlist” means a highly targeted database populated by a specic person or persons
whom there is probable cause to believe have committed, are committing, or are about to commit an
oense that involves the immediate danger of death or serious physical injury to any person.
(g) [“Investigative or law enforcement ocer” means any ocer of a State or a political subdivision
a State or of the United States, who is empowered by law to conduct investigations of or to make
arrests for oenses enumerated in the State criminal code or Title 18 of the U.S. Code, and any
attorney authorized by law to prosecute or participate in the prosecution of such oenses.]
(h) [“State investigative or law enforcement ocer” means any ocer of the State or a political
subdivision the State who is empowered by law to conduct investigations of or to make arrests for
oenses enumerated in the State criminal code, and any attorney authorized by law to prosecute or
participate in the prosecution of such oenses.]
Title I. Use of Face Recognition by Law Enforcement
Section 101. Targeted Face Recognition.
(a) Arrest photo databases.—
(1) General. [A state/Any] investigative or law enforcement ocer shall not use or request targeted
face recognition in conjunction with an arrest photo database except as provided in this section.(2)
(2) Permitted uses. [A state/Any] investigative or law enforcement ocer may use or request targeted face
recognition in conjunction with an arrest photo database maintained pursuant to paragraph (3)—
(A) To identify any individual whom the ocer encounters in person under circumstances which
provide the ocer a reasonable suspicion that the person has committed, is committing or is
about to commit a criminal oense;
(B) To identify any individual whom the ocer reasonably suspects has committed, is
committing or is about to commit an oense punishable by imprisonment for more than one
year.
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(3) Any custodian of an arrest photo database used by or at the request of an investigative or law
enforcement ocer in conjunction with targeted face recognition shall, every six months, eliminate
from that database photos of persons—
(A) Released without a charge;
(B) Released after charges are dropped or dismissed or a nolle prosequi notice is entered; or
(C) Not convicted of the charged oense.
(b) Identication Photo Databases.
1
—
(1) General. Any investigative or law enforcement ocer, state or federal, shall not use or request
targeted face recognition in conjunction with [a state/an] identication photo database except as
provided in this section.
(2) Permitted uses. [Except as provided by paragraph (4),] an investigative or law enforcement ocer,
state or federal, may use or request targeted face recognition in conjunction with [a state/an]
identication photo database pursuant to an order issued under paragraph (3);
(3) Orders.—
(A) Authority. [Except as provided by paragraph (4),] the principal prosecuting attorney of
[the/any] State or any political subdivision thereof and any attorney for the Government
(as such term is dened for the purposes of the Federal Rules of Criminal Procedure), is
authorized to make an application to a [Name of State/State or federal] judge of competent
jurisdiction for, and such judge may grant in conformity with subparagraph (C), an
order authorizing the use of targeted face recognition in conjunction with a [a state/an]
identication photo database [within the jurisdiction of that judge] to identify any person
whom there is probable cause to believe has committed, is committing, or is about to commit
an oense enumerated in subsections (1) and (2) of section 2516 of Title 18 of the U.S.
Code.
(B) Application. Each application for an order authorizing the use of targeted face recognition
in conjunction with a [state] identication photo database shall be made in writing upon
oath or armation to a judge of competent jurisdiction and shall state the applicant’s
authority to make such application. Each application shall include the following
information:
(i) e identity of the investigative or law enforcement ocer making the application, and
the ocer authorizing the application;
1. Authors’ note: To prohibit the use of driver’s license and ID photos for criminal face recognition searches, delete the text in this subsection and
include in its place the following statement: “Any investigative or law enforcement ocer, state or federal, shall not use targeted face recognition
in conjunction with a [state] identication photo database, or acquire in bulk the photos in that database.” To institute a truly total ban, even in
emergency cases, you will also have to amend the exceptions set out at subsection (c). To allow limited use of these photos, include the language
set out in subsection (b).
105
(ii) As full and complete description as possible of the person or persons that the ocer
seeks to identify;
(iii) A full and complete description of the photos or video portraying that person or persons
that will be used to search the [state] identication photo database;
(iv) A full and complete statement as to whether or not other investigative procedures
to identify that person or persons, including the use of targeted face recognition in
conjunction with an arrest photo database, have been tried and failed or why they
reasonably appear to be unlikely to succeed;
(v) e specic [state] identication photo database or databases to be searched
[, and, in the case of an application to access a state identication photo database
transmitted to a federal judge, a certication that the individuals portrayed in that
database primarily reside within the jurisdiction of the judge or in the same federal
circuit].
(vi) e particular oense enumerated in subsections (1) and (2) of section 2516 of the U.S.
Code that are being investigated; and
(vii) A full and complete statement of the facts and circumstances that provide the ocer
probable cause to believe that the person or persons have committed, are committing, or
are about to commit that oense or oenses.
(C) Issuance.—
(i) Upon such application the judge may enter an ex parte order, as requested or as
modied, authorizing or approving the use of targeted face recognition in conjunction
with [a state/an] identication photo database [within the jurisdiction of that judge or in
the same federal circuit], if the judge determines that—
(I) there is probable cause to believe that the person or persons described committed,
are committing, or are about to commit a particular oense or oenses enumerated
in subsections (1) and (2) of section 2516 of Title 18 of the U.S. Code; and
(II) normal investigative procedures, including the use of targeted face recognition
in conjunction with an arrest photo database, have been tried and have failed or
reasonably appear to be unlikely to succeed.
(ii) Each order authorizing or approving such use shall state—
(I) e identity of the state or federal law enforcement agency authorized to conduct
targeted face recognition, and of the ocer authorizing the application;
(II) e authority under which the order is made;
(III) In as much detail as necessary, the person or persons that the ocer seeks to identify;
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(IV) e photos or video portraying that person or persons that will be used to search
the identication photo database, and a prohibition on the use of future photos or
video or other any photos or video not specically listed in the order;
(V) e [state] identication photo database or databases to be searched;
(VI) e period of time within which the agency must execute the search, not to exceed
seven days; and
(VII) e particular oense enumerated in subsections (1) and (2) of section 2516 of
Title 18 of the U.S. Code that are being investigated.
(D) Notice to the Public. [e] State department[s
] of motor vehicles shall post notices in
conspicuous locations at all department driver licensing oces, make written information
available to all applicants at department driver licensing oces, and provide information on
the department[s’] web site[s] regarding [state] investigative or law enforcement ocers’
searches of driver’s license and ID photos through targeted face recognition. e notices,
written information, and online information must address how ocers' use and access
targeted face recognition in criminal investigations.
(E) Conforming Amendments.
2
—[e Driver’s Privacy Protection Act, section 2721 of Title
18 of the U.S. Code, shall be amended as follows—
(i) Insert after subparagraph (a)(2) the following subparagraph: “(3) a department-
operated face recognition system, except as provided in subsection (c) of this section”;
(ii) Insert at the end of subparagraph (b)(1) the following text: “but if the personal
information or highly restricted personal information to be disclosed is a person’s
photograph to be used or enrolled in a law enforcement face recognition system, only
on a case-by-case basis that does not involve the bulk transfer of persons’ photographs
to a state or federal law enforcement agency or a third party entity that will allow law
enforcement to access those photographs for the purposes of face recognition”; and
(iii) Insert after subsection (b) the following section: “(c) Law Enforcement Access
to Face Recognition Systems.— A State department of motor vehicles, and any
ocer, employee, or contractor thereof, may make available a department-operated
face recognition system to a state or federal law enforcement agency, or perform
searches of such a system on behalf of the agency, only pursuant to an order issued
under subparagraph (b)(3) of Title 1 of the Face Recognition Act, or pursuant to the
exceptions enumerated in subsection (c) of that Act.”]
2. Authors’ note: e federal government has a driver’s privacy law (18 U.S.C. 2721); most states do, too. To prevent loopholes, that law has to
be amended to ensure (1) that DMV face recognition systems only allow law enforcement to search or request searches of their face recognition
systems pursuant to this statute; and (2) that law enforcement agencies do not transfer, in bulk, the photos in those systems to themselves or a
third party. e language below amends the federal driver’s privacy law to achieve that objective. For state bills, the state driver’s privacy law will
need to be amended in a similar manner.
107
(4) State Law Preserved.—[e authorities provided by paragraphs (2) and (3) of this subsection
do not authorize access to state identication databases maintained by a State, or a political
subdivision of a State, where state law prohibits law enforcement—
(A) access to driver’s license and identication document photos; or
(B) use of face recognition to conduct searches of those photos.]
(c) Emergencies and exceptions.—
(1) Notwithstanding subsections (a) and (b), [a state/an] investigative law enforcement ocer may
use or request targeted face recognition in conjunction with an arrest photo database, and an
investigative law enforcement ocer, state or federal, may use or request targeted face recognition
in conjunction with a [state] identication photo database—
(A) To identify any person who is deceased, incapacitated or otherwise physically unable of
identifying himself, or the victim of a crime, whom the ocer determines, in good faith,
cannot be identied through other means;
(B) To identify a minor whom the ocer believes, in good faith, is the subject of an AMBER
Alert, as that term is used in section 5791 of Title 42 of the U.S. Code;
(C) To identify any person who has been lawfully arrested, during the process of booking that
person after an arrest or during that person’s custodial detention; or
(D) To conduct targeted face recognition in conjunction with [a state/an] identication photo
database to identify any person—
(i) if the principal prosecuting attorney of [the/any] State or any political subdivision
thereof, or any attorney for the Government (as such term is dened for the purposes
of the Federal Rules of Criminal Procedure) determines that an emergency situation
exists that involves immediate danger of death or serious physical injury to any person;
or that requires the use of targeted face recognition in conjunction with an identication
photo database to occur before an order authorizing such use can, with due diligence, be
obtained; and
(ii) there are grounds upon which an order could be entered under this chapter to authorize
such use.
(2) If an investigative or law enforcement ocer uses targeted face recognition pursuant to
subparagraph (D) above, he shall apply for an order approving the use under paragraph (b)(3)
above within twelve hours after the use occurred. e use shall immediately terminate when the
application for approval is denied, or in the absence of an application, within twelve hours. In cases
where an order is not obtained, the ocer shall destroy all information obtained as a result of the
search.
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(3) [e authority provided by paragraph (1) shall authorize access to state identication databases
maintained by a State, or a political subdivision of a State, where state law prohibits law
enforcement—
(A) access to driver’s license and identication document photos; or
(B) use of face recognition to conduct searches of those photos.]
Section 102. Continuous Face Recognition.
3
(a) General. [A state/any] investigative or law enforcement ocer shall not use or request continuous
face recognition [within the State] except as provided in this section.
(b) Permitted uses. [A state/any] investigative or law enforcement ocer may use or request continuous
face recognition pursuant to an order issued under paragraph (3);
(1) Authority. e principal prosecuting attorney of [the/any] State or any political subdivision
thereof [, and the Attorney General of the United States, Deputy Attorney General, Associate
Attorney General, or any Assistant Attorney General, any acting Assistant Attorney General,
or any Deputy Assistant Attorney General or acting Deputy Assistant Attorney General in the
Criminal Division or National Security Division specially designated by the Attorney General,]
is authorized to make an application to a [Name of State/state or federal] judge of competent
jurisdiction for, and such judge may grant in conformity with paragraph (3), an order authorizing
the use of continuous face recognition within [the State/that judge’s jurisdiction] in conjunction
with a emergency watchlist.
(2) Application. Each application for an order authorizing continuous face recognition shall be
made in writing upon oath or armation to a judge of competent jurisdiction and shall state
the applicant’s authority to make such application. Each application shall include the following
information:
(A) e identity of the investigative or law enforcement ocer making the application, and the
ocer authorizing the application;
(B) e number of persons on the emergency watchlist;
(C) As full and complete description as possible of the person or persons on the emergency
watchlist, or their identities, if known, and the photos or video through which they have
been enrolled on the emergency watchlist;
(D) A full and complete description of the nature and specic locations within [the State/the
judge’s jurisdiction] where continuous face recognition will be performed;
3. Authors’ note: To prohibit the use of continuous face recognition, delete the text in this section and include in its place the following statement:
“(a) General.—[A state/any] investigative or law enforcement ocer shall not use continuous face recognition [within the State].” To institute a
truly total ban, even in emergency cases, you will also have to amend the exceptions set out at subsection (c). To allow limited use of continuous
face recognition, include the language set out below.
109
(E) A statement of the period of time for which the continuous face recognition is required to be
maintained;
(F) A full and complete statement as to whether or not other investigative procedures to locate
the person or persons on the emergency watchlist have been tried and failed or why they
reasonably appear to be unlikely to succeed;
(G) e particular oense involving the immediate danger of death or serious bodily injury that
are being investigated;
(H) A full and complete statement of the facts and circumstances that—
(i) provide the ocer probable cause to believe that the person or persons have committed,
are committing, or are about to commit that oense or oenses; and
(ii) give reason to believe that an emergency situation exists that requires the use of
continuous face recognition without delay;
(I) Where the application is for the extension of an order, a statement setting forth the results
thus far obtained from the continuous face recognition, or a reasonable explanation of the
failure to obtain such results.
( J) e judge may require the applicant to furnish additional testimony or documentary evidence
in support of the application.
(3) Issuance.—
(A) Upon such application the judge may enter an ex parte order, as requested or as modied,
authorizing or approving the use of continuous face recognition within [the State/that
judge’s jurisdiction] in conjunction with a emergency watchlist, if the judge determines
that—
(i) there is probable cause to believe that the specic person or persons on the emergency
watchlist committed, are committing, or are about to commit a particular oense
involving the immediate danger of death or bodily injury to any person;
(ii) normal investigative procedures to locate the person or persons on the emergency
watchlist, have been tried and have failed or reasonably appear to be unlikely to succeed;
and
(iii) an emergency situation exists that requires the use of continuous face recognition
without delay.
(B) Each order authorizing or approving such use shall specify—
(i) e identity of the law enforcement agency authorized to conduct continuous face
recognition, and of the ocer authorizing the application;
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(ii) e authority under which the order is made;
(iii)In as much detail as necessary, the person or persons on the emergency watchlist, or their
identities, if known, and the photos or video through which they have been enrolled on
the emergency watchlist;
(iv) e nature and specic locations within [the State/that judge’s jurisdiction] where
continuous face recognition will be performed;
(v) e particular oense that is being investigated; and
(vi) e period of time during which such continuous face recognition is authorized.
(C) No order entered pursuant to this paragraph may authorize or approve continuous
face recognition for any period longer than is necessary to achieve the objective of the
authorization, nor in any event longer than 7 days. Extensions of an order for a maximum of
7 days may be granted, but only upon application for an extension made in accordance with
paragraph (2) of this subsection and the court making the ndings required by subparagraph
(3)(A) of this subsection.
(D) Whenever an order authorizing continuous face recognition is entered pursuant to paragraph
(3), the order may require reports to be made to the judge who issued the order showing
what progress has been made toward the achievement of the authorized objective and the
need for ongoing continuous face recognition. Such reports shall be made at such intervals as
the judge may require.
(c) Emergencies and exceptions.—
1. Notwithstanding subsections (a) and (b), [a state/any] investigative law enforcement ocer may
use or request continuous face recognition in conjunction with a emergency watchlist if—
(A) the principal prosecuting attorney of [the/any] State or any political subdivision thereof [, or
the Attorney General of the United States, Deputy Attorney General, Associate Attorney
General, or any Assistant Attorney General, any acting Assistant Attorney General, or
any Deputy Assistant Attorney General or acting Deputy Assistant Attorney General in
the Criminal Division or National Security Division specially designated by the Attorney
General,] determines that—
(i) an emergency situation exists that involves immediate danger of death or serious bodily
injury to any person;
(ii) that requires the use of continuous face recognition in conjunction with a emergency
watchlist before an order authorizing such use can, with due diligence, be obtained; and
(B) there are grounds upon which an order could be entered under this chapter to authorize such
use.
111
(2) If [a state/an] investigative or law enforcement ocer uses continuous face recognition pursuant
to subparagraph (1) above, the ocer shall apply for an order approving the use under paragraph
(b)(2) above within twelve hours after the use occurred or began. e use shall immediately
terminate when the application for approval is denied, or in the absence of an application, within
twelve hours. In cases where an order is not obtained, the ocer shall destroy all information
obtained as a result of the search.
Section 103. Civil Rights and Civil Liberties. [A state/an] investigative or law enforcement ocer shall not—
(a) use face recognition to create a record describing how any individual exercises rights guaranteed by
the First Amendment unless expressly authorized by statute or by the individual for whom the record
is created or unless pertinent to and within the scope of an authorized law enforcement activity where
there is reasonable suspicion to believe the individual has engaged, is engaging, or is about to engage
in criminal activity; or
(b) rely on actual or perceived race, ethnicity, national origin, religion, disability, gender, gender identity,
or sexual orientation in selecting which person to subject to face recognition, except when there is
reasonable suspicion, relevant to the locality and timeframe, that links a person with a particular
characteristic described in this subsection to an identied criminal incident or scheme.
Section 104. Logging of Searches. [A state/an] law enforcement agency whose investigative or law
enforcement ocers use targeted or continuous face recognition shall log its use of the technology to the extent
necessary to comply with the public reporting and audit requirements of sections 105 and 106 of this Act.
Section 105. Public Reporting.
(a) In January of each year, any judge who has issued an order under subparagraph (b)(3)(C) of section
101 of this Act or an order (or extension thereof) under paragraph (b)(3) of section 102 of this
Act in the preceding calendar year, or who has denied approval of an application for such orders or
extensions during that period, shall report to [the chief judge of the highest court of the State/the
Administrative Oce of the United States Courts]—
(1) the fact that an order or extension was applied for;
(2) whether the order or extension was issued pursuant to subparagraph (b)(3)(C) of section 101 of
this Act, or paragraph (b)(3) of section 102 of this Act;
(3) the fact that the order or extension was granted as applied for, was modied, or was denied;
(4) the oense specied in the order or application, or extension of an order;
(5) the identity of the applying investigative or law enforcement ocer and agency making the
application and the person authorizing the application;
(6) for orders issued pursuant to subparagraph (b)(3)(C) of section 101 of this Act, the [state]
identication photo database that was searched;
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(7) for orders issued pursuant to subparagraph (b)(3) of section 102 of this Act—
(A) the number of persons in the emergency watchlist;
(B) the nature and specic locations [within the State] where continuous face recognition was
performed; and
(C) the period of time during which continuous face recognition was performed.
(b) In March of each year, the principal prosecuting attorney for [the/a] State, or the principal
prosecuting attorney for any political subdivision of [the/a] State [, and the Attorney General, an
Assistant Attorney General specially designated by the Attorney General], shall report to [the chief
judge of the highest court of the State/the Administrative Oce of the United States Courts], with
respect to the preceding calendar year—
(1) For the use targeted face recognition in conjunction with an arrest photo database—
(A) the number of such searches run;
(B) the oenses that those searches were used to investigate, and for each oense, the number of
searches run;
(C) the arrests that resulted from such searches, and the oenses for which arrests were made;
(D) the number of convictions resulting from such interceptions and the oenses for which the
convictions were obtained; and
(E) the number of motions to suppress made with respect to those searches, and the number
granted or denied.
(2) For orders granted under subparagraph (b)(3)(C) of section 101 for targeted face recognition in
conjunction with [a state/an] identication photo database, for each order—
(A) the information specied in paragraphs (1) through (6) of subsection (a); and
(B) the information specied in subparagraphs (B) through (E) of paragraph (1) in this
subsection;
(3) For orders or extensions of orders granted under paragraph (b)(3) of section 102 for continuous
face recognition, for each order—
(A) the information specied in paragraphs (1) through (5) and (7) of subsection (a); and
(B) the information specied in subparagraphs (B) through (E) of paragraph (1) in this subsection.
(c) In June of each year [the chief judge of the highest court of the State/the Administrative Oce of the
United States Courts] shall release to the public, post online, and transmit to [the State Legislature/
the Congress] a full and complete report concerning the use of targeted and continuous face
113
recognition in conjunction with arrest photo databases, [state] identication databases, and emergency
watchlists, including—
(1) the number of applications for orders or extensions authorizing or approving targeted face
recognition in conjunction with [a state/an] identication photo database or continuous face
recognition in conjunction with an emergency watchlist and the number of orders and extensions
granted or denied pursuant to this Act during the preceding calendar year.
(2) a summary and analysis of the data required to be led with [the chief judge of the highest court
of the State/the Administrative Oce of the United States Courts] by subsections (a) and (c) of
this section and sections 106 and 107 of this Act.
(d) e [the chief judge of the highest court of the State/the Administrative Oce of the United States
Courts] is authorized to issue binding regulations dealing with the content and form of the reports
required to be led by subsections (a) and (c) of this section and sections 106 and 107 of this Act.
Section 106. Audits. Any [state] law enforcement agency whose [state] investigative or law enforcement
ocers use targeted or continuous face recognition, regardless of whether they use a system operated by
that agency or another agency, shall annually audit that use to prevent and identify misuse and to ensure
compliance with sections 101, 102, and 103 of this Act, and shall report—
(a) a summary of the ndings of the audit, including the number and nature of violations identied,
to [the chief judge of the highest court of the State/the Administrative Oce of the United States
Courts], and subsequently release that information to the public and post it online; and
(b) any violations identied to [the principal prosecuting attorney for the State/the Attorney General].
Section 107. Accuracy and Bias Testing.
(a) Any [state] law enforcement agency whose [state] investigative or law enforcement ocers operate a
system of targeted or continuous face recognition shall regularly submit that system to independent
testing to determine—
(1) the accuracy of the system; and
(2) whether the accuracy of the system varies signicantly on the basis of race, ethnicity, gender or age.
(b) A summary of the ndings of the tests required by subsection (a) shall be submitted to the [the
chief judge of the highest court of the State/the Administrative Oce of the United States Courts],
released to the public, and posted online.
Section 108. Enforcement.
(a) Suppression. Whenever targeted or continuous face recognition has occurred, no results from those
searches and no evidence derived therefrom may be received in evidence in any trial, hearing, or other
proceeding in or before any court, grand jury, department, ocer, agency, regulatory body, legislative
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committee, or other authority of the United States, a State, or a political subdivision thereof if the
use of face recognition violated sections 101, 102 or 103 of this Act or if the use was conducted in an
emergency pursuant to subparagraph (c)(1)(D) of section 101 or paragraph (c)(1) of section 102 and
the ocer or agency did not subsequently obtain an order for that use as required by paragraph (c)(2)
of section 101 and section (c)(2) of section 102.
(b) Administrative Discipline. If a court or law enforcement agency determines that an investigative or
law enforcement ocer has violated any provision of this Act, and the court or agency nds that the
circumstances surrounding the violation raise serious questions about whether or not the ocer acted
willfully or intentionally with respect to the violation, the agency shall promptly initiate a proceeding
to determine whether disciplinary action against the ocer is warranted.
(c) Civil Action.
(1) In General. Any person who is subject to targeted identication or attempted identication
through targeted continuous face recognition in violation of this Act may in a civil action recover
from the [state] investigative or law enforcement ocer or the state or [federal law] enforcement
agency which engaged in that violation such relief as may be appropriate.
(2) Relief. In an action under this subsection, appropriate relief includes—
(i) such preliminary and other equitable or declaratory relief as may be appropriate;
(ii) damages under subparagraph (2) and punitive damages in appropriate cases; and
(iii) a reasonable attorney’s fee and other litigation costs reasonably incurred.
(3) Computation of Damages. e court may assess as damages whichever is the greater of—
(i) the sum of the actual damages suered by the plainti and any prots made by the
violator as a result of the violation; or
(ii) statutory damages of whichever is the greater of $500 a day for each day of violation or
$50,000;
(4) Defense. A good faith reliance on—
(i) a court warrant or order, a grand jury subpoena, a legislative authorization, or a statutory
authorization; or
(ii) a good faith determination that subsection (c) of section 101 or subsection (c) of section
102 of this Act permitted the conduct complained of;
is a complete defense against any civil or criminal action brought under this chapter.
(5) Limitation. A civil action under this section may not be commenced later than two years after the
date upon which the claimant rst has a reasonable opportunity to discover the violation.
115
Title II. Funding for Law Enforcement Face Recognition Systems and Research
Section 201. Law Enforcement.
4
(a) No [state/federal] nancial assistance or funds may be expended for the creation, maintenance, or
modication of a law enforcement face recognition system unless the agency operating that system—
(1) certies compliance with sections 104, 105, 106 and 107 of this Act;
(2) certies that the algorithm employed by its face recognition system has been submitted for testing
in the most recent Face Recognition Vendor Test administered by the National Institute of
Standards and Technology;
(3) provides documentation to conrm that the agency has released to the public and posted online
a use policy governing its use of face recognition and, in the case of a law enforcement agency
serving a subdivision of [a/the] State, has secured approval for that policy from a city council or
other body primarily comprised of elected ocials.
(b) Subsection (a) shall take eect 18 months after the enactment of this Act, except for paragraph (2) of
that subsection, which shall take eect ve years after enactment.
[Section 202. e National Institute for Standards and Technology
(a) e National Institute of Standards and Technology (NIST) shall—
(1) Develop best practices for law enforcement agencies to evaluate the accuracy of their face
recognition systems;
(2) Oer biennial Face Recognition Vendor Tests to evaluate the accuracy of face recognition
algorithms;
(3) Develop, and implement as part of Face Recognition Vendor Tests, evaluations of whether the
accuracy of a face recognition algorithm varies on the basis of race, ethnicity, gender or age; and
(4) Develop large, high-quality publicly available datasets of facial images to support NIST accuracy
and bias testing and similar testing conducted by independent entities.
(b) ere is authorized to be appropriated to the National Institute of Standards and Technology to
carry out subsection (a) $_____________ for each of the scal years 2018, 2019, 2020 and 2021.]
4. Authors’ Note: is section (1) uses the power of the purse to condition federal or state funding for law enforcement face recognition on
agencies’ adopting measures to ensure transparency and accountability; and, in the case of federal legislation, (2) authorizes additional funding
for the National Institute of Standards and Technology. Appropriations legislation is highly complex; this language will have to be adapted to the
state in which it is oered. Furthermore, these provisions, particularly section 201, may best function as stand-alone amendments to other funding
legislation. If these subsections are indeed oered as “riders,” additional provisions may be added that incorporate some of the protections of
sections 101, 102 and 103 of this legislation.
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PURPOSE.
(a) To establish procedures for the
acceptable use of the images,
information, and tools within the face
recognition system.
300
BACKGROUND.
(a) Face recognition refers to an automated
process of matching face images utilizing
algorithms and biometric scanning
technologies.
301
(b) is face recognition system was
established [date] in conjunction with
[other agency partners, if applicable]. e
system helps identify possible criminal
suspects or unidentied victims.
302
(c) Personnel from the following agencies
can request face recognition searches:
i. [List; date of last update to list]
SCOPE.
is policy applies to all law enforcement
personnel who are granted direct access to the
face recognition system as well as personnel
who are permitted to request face recognition
searches. Any outside agency, or personnel from
an outside agency, requesting face recognition
assistance with an investigation must adhere to
this policy.
303
DATABASE AND DATA
LIMITATIONS.
(a) e face recognition system runs searches
against a database of mug shots that
is updated twice per year to eliminate
proles of individuals who were not
charged, had charges against them
dropped, or who were found not guilty.
Proles of individuals whose records
have been expunged are also removed
immediately upon expungement.
304
(b) No other databases, such as driver’s
licenses photo databases, are linked to
or accessible via the face recognition
system.
305
(c) Probe photos are the images of unknown
suspects or victims that are submitted
for comparison against the mug shot
database. Probe photos are not enrolled
into the face recognition database.
i. Unsolved Photo File. A probe photo
of an unknown suspect may be added
to an unsolved photo le if there is
probable cause to believe that suspect
has committed a felony. Photos in
this le are searched against new
mug shot enrollments and future
face recognition probe photos in
an attempt to identify the photo
suspect. Once the individual has been
identied, the image shall be removed
from the le.
306
XII. MODEL FACE RECOGNITION USE POLICY
117
(d) ere is no interface of the face
recognition system to any form of video
surveillance, including surveillance
cameras, drone footage, and body worn
cameras.
307
e system will not be
congured to conduct real-time or near-
real-time face recognition analysis on live
video.
(e) Potential matches returned by the face
recognition system are to be considered
investigative leads only, and cannot be
used as the sole basis for an arrest.
ACCEPTABLE USES OF FACE
RECOGNITION.
1. Field Identication (mobile searches). Face
recognition may be used on a mobile device
as an identication tool by an ocer in the
eld in one of three instances:
(a) When an individual consents to have his
or her photograph taken for the purpose
of identication.
i. If consent is withdrawn, use of face
recognition is not authorized and its
use must stop immediately.
308
(b) When the ocer reasonably believes an
individual is concealing his or her true
identity and has reasonable suspicion the
individual has committed a crime other
than concealing his or her identity.
i. An ocer must rst attempt to
ascertain the individual’s identity by
means other than face recognition,
such as requesting identication.
(c) When an individual is unable to
provide reliable identication due to
physical incapacitation or defect, mental
incapacitation or defect, or death, and
immediate identication is needed to
assist the ocer in performance of his or
her lawful duties.
309
Field photographs shall be taken and stored
or deleted in accordance with the relevant
department policies and procedures.
2. Investigative Searches. Face recognition
may be used as an investigative tool
to identify suspects caught on camera
committing a felony. Investigative searches
may be conducted in one of two instances:
(a) When an ocer has reasonable suspicion
that the suspect to be searched has
committed a felony.
(b) When the subject to be searched is
believed to be a victim of a crime.
Investigative searches shall only be conducted by
trained Face Examiners. Face Examiner refers to
an individual who has received advanced training
in the face recognition system and its features.
Examiners have at least a working knowledge of
the limitations of face recognition and the ability
to use image editing software. ey are qualied
to assess image quality and appropriateness for
face recognition searches and to perform one-to-
many and one-to-one face image comparisons.
Examiners determine if images are suitable for
face recognition searches, and may enhance
images for the purpose of conducting a face
recognition search. ough enhancements to
the probe image are permissible, the examiner
shall not base any conclusions on a comparison
between an enhanced image and a potential
candidate image. All human comparison shall
take place between the original unknown image
and the potential candidate image.
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Examiners shall submit the conclusions of their
analyses for peer review for investigative leads
resulting from one-to-many searches and both
peer review and administrative review for one-
to-one comparisons. Examiners shall return
a maximum of two images to the requesting
ocer or agency to be used as investigative leads
only.
310
3. Arrest. Face recognition may be used upon
the arrest of an individual to identify that
individual and/or determine whether he or
she has previously been arrested and charged
with a crime. Since the probe photo is also
a mug shot, it may be enrolled in the face
recognition database, but must be removed if
the individual is never charged or convicted,
or if the record is expunged.
FACE RECOGNITION PRIVACY
PROTECTIONS AND OVERSIGHT.
1. Constitutional Guarantees.
(a) Face recognition is uniquely powerful
investigative and identication tool. Law
enforcement ocers shall not employ
this technology to conduct dragnet
screening of individuals, nor shall they
use it to facilitate mass surveillance of
places, groups or activities unless doing
so furthers an ocial law enforcement
activity and is conducted in accordance
with this policy.
i. For example, it would not be
appropriate for ocers to use face
recognition technology to conduct
surveillance of persons or groups
based solely on their religious,
political or other constitutionally
protected activities,
311
or their
race, ethnicity, gender, or sexual
orientation.
(b) Face recognition must be used in
accordance with all federal and state
laws, and all departmental policies,
including those addressing improper
racial proling.
312
2. Training.
(a) All personnel who are authorized to run
a eld identication (mobile) search or
request an investigative search shall be
trained in the following areas prior to
utilizing face recognition:
i. e proper and lawful use of face
images for face recognition purposes;
ii. How to take high quality face images
in the eld for most accurate results;
iii. e appropriate use and sharing of
information obtained from a face
recognition search;
iv. e deletion of a eld identication
probe image used for comparison.
(b) Personnel who have not received this
training shall not utilize face recognition
or request an investigative search.
313
3. Audits and Penalties for Misuse.
(a) All face recognition use and search
requests are subject to audit. In the event
of an audit, the user will be required
to provide appropriate justication for
the use or request of a face recognition
search.
Appropriate justication shall include a
situation description and purpose for the
search, including a detailed account of
circumstances amounting to reasonable
suspicion, and a case/complaint number
and le class/crime type, if available. For
119
searches conducted on behalf of another
individual, the name and rank/job title
of other individual requesting the search
shall also be included.
314
(b) Penalties for misuse that may be
imposed include, but are not limited to,
termination of a user’s access to the face
recognition system or the termination of
agency-wide access to the system.
315
4. Approvals and Transparency.
is policy shall be reviewed annually by [City
Council or other body primarily comprised of
elected ocials]. e policy, and any updates to
it, shall be made publicly available.
316
e Facial Analysis, Comparison, and Evaluation (FACE) Services Unit runs face recognition searches for other FBI divisions
and federal partners. FACE Services can search or request searches of 411 million photos, including 24.9 million mug shots,
over 140 million visa photos and over 185 million state driver’s license and ID photos in: Alabama; Arkansas; Delaware; Illinois;
Iowa; Kentucky; Michigan; Nebraska; New Mexico; North Carolina; North Dakota; South Carolina; Tennessee; Texas; Utah; and
Vermont (GAO). As of 2014, there were 64 million licensed drivers in those states (FHWA). is means that almost one in three
U.S. drivers’ photos can be searched by FACE Services.
e FBI does not require reasonable suspicion to run a search; searches can be run in cases where “allegation or information”
indicates possible criminal activity (FBI). While the FBI has not evaluated the accuracy of the state face recognition systems it
relies on, this is partially oset by the fact that search results are manually reviewed by a FACE Services specialist before the top
one or two candidate photos are sent to the requesting FBI agent as a potential lead (GAO). In a March 2015 Privacy Impact
Assessment, the FBI asserted that audit procedures were in place to prevent misuse (FBI). A summer 2016 GAO investigation
found that in the previous four and a half years, the FBI had not, in fact, audited its use of these databases (GAO).
Sources and Notes: FBI FACE Services Privacy Impact Assessment, Federal Highway Administration, GAO (Last updated:
September 2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial
records available online at www.perpetuallineup.org.
FEDERAL BUREAU OF INVESTIGATION
FACE SERVICES UNIT
JURISDICTION
e FBI can search 411 million photos, including 24.9
million mug shots, 140 million visa photos and over
185 million state driver’s license and ID photos.
e FBI does not require reasonable suspicion to run a
search.
e FBI has searched state license and ID databases
over 36,000 times but has not audited those searches
for misuse.
XIII. CITY & STATE BACKGROUNDERS
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e Maricopa County Sheri ’s Oce rst purchased a face recognition system in 2006 (015026). MCSO can search 14.5 million
Arizona driver’s license photos, 3.2 million Arizona mug shots, and other databases, such as the DOJ’s Federal Joint Automated
Booking System, which contains another 1.5 million booking photos (014954). In 2007, MCSO also enrolled all Honduran driver’s
license and booking photos, provided by the Honduran government. (015058). Reasonable suspicion is not required for the MCSO
to run a face recognition search (014949). African Americans are likely overrepresented in the system; they are arrested in Arizona
at a rate 170% higher than their population share.
A specialized Facial Recognition Unit (FRU) runs the searches. Reviewers are instructed to “review the results and identify possible
leads” and receive supervisor approval before returning possible leads to the requester (014963–014964). ere are no records of any
audits being conducted on the system (014949). e Arizona Department of Public Safety previously submitted face recognition
search requests to the FRU, but in response to our records request indicated that it has not done so since 2013 (010717).
We understand that MCSO uses a system initially provided by Hummingbird Defense Systems, but it is unclear if this is the
current provider, as well as what algorithm the system uses (014976).
Sources and Notes: Maricopa County Sheri ’s Oce, Arizona Department of Public Safety, U.S. Census (Last updated: September
2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records
available online at www.perpetuallineup.org.
MARICOPA COUNTY SHERIFF'S
OFFICE (MCSO)
JURISDICTION
Police can search 14.5 million driver’s license and ID
photos, 3.2 million mug shots, and other databases.
Police are not required to have reasonable suspicion to
run a search.
In 2007, Maricopa County enrolled all driver’s license
photos and mug shots from Honduras in its databases.
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e Arkansas State Police (ASP) can search 24.9 million mug shot photos in the FBI’s face recognition database, known as the
Next Generation Identication Interstate Photo System, or NGI-IPS (GAO). According to a 2016 report by the Government
Accountability Oce, the FBI can also request searches of Arkansas’ 15.4 million driver’s license and ID photos (GAO).
In response to our public records request, ASP stated it “does not currently use” face recognition technology (000100).
Sources and Notes: ASP, GAO (Last updated: September 2016). Scorecard methodology can be found in the report on page 93.
Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
ARKANSAS STATE POLICE (ASP)
JURISDICTION
Police can search 24.9 million mug shots in the FBI
database.
e FBI can search 15.4 million driver’s license and ID
photos.
ASP stated it “does not currently use” face recognition
technology.
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e Los Angeles Police Department (LAPD) may have the most advanced face recognition system in the country—yet refused
to comply with our public records request. In 2013, it announced a system that could perform real-time face recognition from
600 feet using CCTV cameras in the West San Fernando Valley. In 2007, LAPD said that a “smart car” would be equipped with
face recognition. In 2005, LAPD announced a CCTV camera system “equipped with ‘intelligent’ video capabilities and facial
recognition software.” Yet in response to a request for documents relating to its use of face recognition, LAPD stated that it has “no
records responsive to [the] request” (000102).
e Los Angeles County Sheri ’s Department (LACSD) has a face recognition system accessible on desktop and via emails sent
from mobile devices (000623). LACSD has no publicly available policy governing when it is appropriate for police to use the face
recognition, and provided no such policy in response to our request. It is possible that it does not exist.
e LACSD system searches mug shots “of all subjects criminally booked within Los Angeles County” (000349), but it’s unclear
what other databases are accessible, how many mug shots are enrolled in the system, or if LACSD “scrubs” its mug shot database to
eliminate people who were never charged, had charges dropped or dismissed, or who were found innocent. African Americans are
likely overrepresented in the system; they are arrested at a rate 176% higher than their share of the county population.
e LACSD system uses NEC and Cognitec algorithms (000444). We do not know what algorithms are employed by the LAPD
systems.
Sources and Notes: LAPD, LACSD, State of California Department of Justice Oce of the Attorney General, U.S. Census (Last
updated: September 2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to
ocial records available online at www.perpetuallineup.org.
LOS ANGELES POLICE DEPARTMENT
(LAPD) & LOS ANGELES COUNTY
SHERIFF'S DEPARTMENT (LACSD)
JURISDICTION
e LAPD has publicly reported use of real-time face
recognition, but found “no records responsive” to our
public records request.
Police in Los Angeles County can run face recognition
searches of mug shots.
It’s unclear if police are required to have reasonable
suspicion to run a search.
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e San Diego Association of Governments (SANDAG) runs the Tactical Identication System (TACIDS) face recognition
system through its Automated Regional Justice Information System (ARJIS). At least 66 agencies have access to the system, at least
28 of which currently run searches (013762, 005699). TACIDS searches a database of 1.4 million mug shots (008449), but does not
allow searches of license photos or real-time face recognition from video feeds (016621). TACIDS is accessible by mobile devices
used by ocers in the eld, and solely designed for identication of arrestees, detainees, and the incapacitated—not after-the-fact
investigations (016619–016620, 008389). Ocers need reasonable suspicion to run a search (005723). e system use policy is
public and must be approved annually by local elected ocials (008453).
e system is not without concerns. SANDAG contracted with a face recognition company, FaceFirst, which claims to have “the
industry’s nest” technology, including an algorithm provided by Cognitec “[w]ith an identication rate above 95%.” However,
that statistic is a decade old, and in its contracts with SANDAG, FaceFirst repeatedly disclaims any liability for “representations
or warranties as to the accuracy” of its face recognition system (008358, 008493). According to the Acceptable Use Policy, while
SANDAG reserves the right to enforce the policy, “[i]dentifying and addressing intentional misconduct is the responsibility of the
individual agency” (008452). However at least one member agency, the Carlsbad Police Department, reported no audits (000149).
African Americans are likely overrepresented in the system; in San Diego County, they’re arrested at a rate 202% higher than their
population share.
Sources and Notes: SANDAG, FaceFirst, NIST, California Oce of the California Attorney General, U.S. Census (Last updated:
September 2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial
records available online at www.perpetuallineup.org.
SAN DIEGO ASSOCIATION OF
GOVERNMENTS (SANDAG)
JURISDICTION
Police can run face recognition searches of 1.4 million
mug shots.
License photo searches are prohibited, as is real-time
video-based face recognition.
Police need reasonable suspicion to run a search.
SANDAG’s use policy must be reviewed and approved
annually by local elected ocials.
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e San Francisco Police Department (SFPD) purchased an Automatic Biometric Identication System (ABIS) in 2010. ABIS
is a multi-biometric system that includes face recognition capabilities. SFPD can search between half a million to one million
mug shots (005501, 005543). It is unclear if reasonable suspicion is required for SFPD to run a search, or if SFPD can search for
witnesses and bystanders. SFPD has no publicly available policy governing when it is appropriate for police to use face recognition,
and provided no such policy in response to our request. It is possible that it does not exist.
During the contracting process for its multi-biometric system, SFPD included requirements that vendor companies submit their
face recognition algorithms to accuracy testing and that their algorithms meet specic accuracy thresholds. e Request for Pro-
posal (RFP) states that on-site tests will be conducted and that “[a]fter system acceptance, accuracy tests will continue to run on a
regular basis to reconrm system performance and detect any degradation” (005557).
African Americans are likely overrepresented in the system; they are arrested at a rate 185% higher than their share of the city
population.
e SFPD system uses an algorithm provided by 3M Cogent (005591).
Sources and Notes: San Francisco Police Department, State of California Department of Justice Oce of the Attorney General,
U.S. Census (Last updated: September 2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g.
(123456), refer to ocial records available online at www.perpetuallineup.org.
SAN FRANCISCO POLICE
DEPARTMENT (SFPD)
JURISDICTION
Police can search half a million to one million mug
shots.
It’s unclear if ocers are required to have reasonable
suspicion to run a search.
SFPD required vendor companies to submit their
algorithms to accuracy testing and meet accuracy
thresholds.
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In 2001, the Pinellas County Sheri ’s Oce (PCSO) launched the Face Analysis Comparison & Examination System (FACES)
(013980). It searches over 33 million faces, including 22 million Florida driver’s license and ID photos and over 11 million law
enforcement photos (014736, 014739). Florida law enforcement can also search the FBI’s database of 24.9 million mug shots
(GAO).
e FBI has not signed an MOU to let its FACE Services unit search the PCSO system. In an apparent workaround, FBI’s Florida
eld oces can search PCSO’s system and are among the 243 local, state, and federal agencies that run close to 8,000 searches per
month (014396). Asked if PCSO audits searches for misuse, Sheri Bob Gualtieri replied, “No, not really.” e Pinellas County
Public Defender’s Oce reports that PCSO has never disclosed use of face recognition in Brady evidence.
FACES users aren’t required to have reasonable suspicion to run a search. e manual encourages ocers to use biometric
identication “whenever practical” (014375). e system does not allow real-time face recognition from video, but this is not
codied in its use manuals.
FACES uses a MorphoTrust algorithm (013791).
Sources and Notes: PCSO, Interviews (Sheri Gualtieri, Public Defender Dillinger), GAO, Washington Post (Last updated:
September 2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial
records available online at www.perpetuallineup.org.
FLORIDA & PINELLAS COUNTY
SHERIFF'S OFFICE (PCSO)
JURISDICTION
Police can run face recognition searches of 22 million
Florida driver’s license and ID photos, over 11 million
mug shots and other photos, and 24.9 million mug
shots in the FBI database.
FBI eld oces in Florida can search the Pinellas
County database, including license and ID photos.
Police and the FBI are not required to have reasonable
suspicion to run a search.
Florida’s database is searched 8,000 times per month.
Searches are not audited for misuse.
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e Honolulu Police Department’s (HPD) face recognition program was established in 2014 in conjunction with the Hawaii
Criminal Justice Data Center (CJDC) (014704). In 2015, the system was expanded to all counties in Hawaii, and accesses a statewide
database of mugshot photos. Driver’s license photos are not included in the database; Hawaii has determined that current statutes,
rules, and regulations prohibit driver’s license and ID card photos from being included in the face recognition system (016846).
African Americans are likely overrepresented in the database; they are arrested at a rate 111% higher than their share of Hawaii’s
population.
HPD requires that police have reasonable suspicion to run a face recognition search, with an exception for “requests that come
directly from the Chief” (016851). Searches are also permitted to identify deceased persons. It is unclear if police can run searches
of bystanders or witnesses. Only sta who have completed face recognition training are permitted to access the system, and
potential matches are manually reviewed by an analyst from the Crime Analysis Unit (014705, 014707). If no match is found in
HPD’s system, a requester can send the image to the FBI to be run against its database of 24.9 million mugshots (014706). Hawaii
signed a Memorandum of Understanding with the FBI in 2011 to participate in the NGI-IPS face recognition pilot.
HPD is one of four agencies we received responsive records from that has made its use policy public. CJDC stated it does not
maintain audit records, and email correspondence with HPD indicates it maintains no records aside from the use policy, which
suggests that no audits are conducted (015310, 016615). e HPD face recognition system uses a Morpho algorithm (016851).
Sources and Notes: HPD, CJDC, State of Hawaii Department of the Attorney General, U.S. Census (Last updated: September
2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records
available online at www.perpetuallineup.org.
HONOLULU POLICE DEPARTMENT
& HAWAII CRIMINAL JUSTICE
DATA CENTER (CJDC)
JURISDICTION
Police can search mugshots.
Police generally need reasonable suspicion to run a
search, with exceptions.
Hawaii is one of four jurisdictions that has made its
use policy public.
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e Chicago Police Department (CPD) uses face recognition, but limits public information on how it is used. CPD has no
public policy governing law enforcement use of face recognition. As of August 2016, phone calls to follow up on public records
requests were directed to a full voicemail account that provides no further contact information. CPD can run face recognition
searches against a mug shot database (008686). It’s unclear if police are required to have reasonable suspicion to run a search,
and if the CPD “scrubs” its mug shot database to eliminate people who were never charged, had charges dropped or dismissed,
or were ultimately found innocent. According to a 2016 report by the Government Accountability Oce, the FBI can request
face recognition searches of 43 million Illinois driver’s license and ID photos (GAO). It’s unclear whether CPD can run searches
against these photos.
In 2012, CPD requested $2 million to support real-time video-based face recognition (008725). ey also bought an exceptionally
large amount of network hardware and spent $450,000 on enterprise-class database and computing infrastructure (008671–
008685). It’s unclear if they currently run face recognition searches against real-time or archival video.
CPD uses a DataWorks Plus face recognition system, but it is unclear which algorithm that system uses.
Sources and Notes: CPD, GAO (Last updated: September 2016). Scorecard methodology can be found in the report on page 93.
Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
CHICAGO POLICE DEPARTMENT
(CPD)
JURISDICTION
CPD can run face recognition searches of mug shots.
e FBI can run face recognition searches of 43
million Illinois driver’s license photos.
It’s unclear if police are required to have reasonable
suspicion to run a search.
In 2012, CPD requested funding for real-time face
recognition. It’s unclear if CPD runs real-time searches.
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Iowa’s face recognition system is owned by the Iowa Department of Transportation (DOT). In 2014, the Iowa Department of Public
Safety (DPS) signed an agreement with the DOT, wherein the DPS would pay for an upgrade to the DOT’s face recognition system
and in exchange gain access to it (008661). is agreement allows authorized DPS personnel to run face recognition searches on
Iowa’s 13 million driver’s licenses and other DOT photos. According to a 2016 report by the Government Accountability Oce, the
FBI can also request searches of Iowa’s driver’s license photos (GAO).
According to DPS, personnel need reasonable suspicion of criminal activity before running a face recognition search (016853).
However, DPS “has not yet adopted a nal policy” governing law enforcement face recognition searches on the grounds that it
is waiting to determine “what uses may be accurate or inaccurate, reliable or unreliable, appropriate or inappropriate” (016854,
011911).
e DOT system uses a MorphoTrust face recognition algorithm (008661).
Sources and Notes: DPS, GAO (Last updated: September 2016). Scorecard methodology can be found in the report on page 93.
Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
IOWA DEPARTMENT OF PUBLIC
SAFETY (DPS)
JURISDICTION
Police can run face recognition searches of 13 million
Iowa driver’s license and ID photos.
e FBI can request searches of 13 million driver’s
license and ID photos.
Police need reasonable suspicion to run a search.
e Iowa Department of Public Safety has not nalized
a face recognition use policy.
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Maine State Police can search 24.9 million mug shots in the FBI’s Next Generation Identication-Interstate Photo System (NGI-IPS)
database (GAO). is database contains mug shots and corresponding ngerprint records submitted by various state and federal
agencies.
e Cumberland County Sheri ’s Oce (CCSO) previously used face recognition, beginning in August 2012. When in use, the
Cumberland County Sheri could search all Cumberland County jail booking photos on le, dating back to 1998 (010631).
e CCSO system used a face recognition system provided by Dynamic Imaging, but it is unclear which company provided the
algorithm used (010634).
Sources and Notes: GAO, Cumberland County Sheri ’s Oce (Last updated: September 2016). Scorecard methodology can be found
in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
MAINE STATE POLICE
JURISDICTION
Police can search 24.9 million mug shots in the FBI
NGI database.
e Cumberland County Sheri’s Oce used to use
face recognition.
A state records request is in process.
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In 2011, the Maryland Department of Public Safety and Correctional Services (DPSCS) began running face recognition searches on
mug shots. In 2013, it enrolled photos from the Maryland Motor Vehicle Administration into the database, a measure that does not
appear to have received any media coverage. e database, the Maryland Image Repository System (MIRS), includes over 7 million
driver’s license and other MVA photos and over 3 million mug shots of “known oenders” (011105). Maryland law enforcement can
also request searches of the FBI’s mug shot database of 24.9 million photos. Many Maryland law enforcement agencies can access
MIRS, including the Maryland State Police and the Baltimore City Police Department (010949). e system is also open to agencies
outside of Maryland. African Americans are likely overrepresented in the system; in Maryland, they are arrested at a rate 75% higher
than their population share. It’s unclear if the DPSCS “scrubs” its mug shot database to eliminate people who were never charged,
had charges dropped or dismissed, or who were found innocent.
Based on a DPSCS fact sheet, it appears searches of the system require probable cause (011104). However, DPSCS did not produce a
use policy in response to our records request. In addition, according to DPSCS, “[t]he MIRS system “has not been audited” in its ve
years of operation (008906).
e DPSCS system uses NEC and Cognitec face recognition algorithms (008892).
Sources and Notes: DPSCS, GAO, Maryland State Police, U.S. Census (Last updated: September 2016). Scorecard methodology
can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at
www.perpetuallineup.org.
MARYLAND DEPARTMENT OF PUBLIC
SAFETY & CORRECTIONAL SERVICE
JURISDICTION
Police can run face recognition searches of over 7
million Maryland driver’s license and ID photos, over
3 million Maryland mug shots, and FBI’s database of
24.9 million mug shots.
In June 2016, GAO reported that the FBI was
negotiating to search Maryland license photos. GAO
retracted that claim at the FBI’s request.
Police likely need probable cause to run a search.
Maryland’s face recognition system has not been
audited since its launch in 2011.
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e Michigan State Police runs the Statewide Network of Agency Photos (SNAP), a database of 4 million mug shots and 41 million
driver’s license and ID photos from the Michigan Department of State. e FBI can request searches of at least 35.6 million of
Michigan’s driver’s license and ID photos (GAO). African Americans are likely overrepresented in SNAP; they’re arrested at a rate
136% higher than their state population share.
Ocers can run searches from a desktop computer or a mobile device. Trained examiners run desktop searches and potential
matches are peer reviewed; results from mobile searches are not peer reviewed (011467–011468). MSP’s face recognition use policy,
which it has made public, requires that an ocer have probable cause before running a mobile search. Desktop searches are only
required to be for a “law enforcement reason” (016824). Searches can be run to identify criminal suspects, witnesses, bystanders,
victims, unknown decedents, and the incapacitated (010928, 011345, 016824).
MSP does not use face recognition with real-time video. In accord with Michigan law (MCL 28.243.2), the MSP deletes from its
database mug shots and ngerprints of people who are arrested but never charged or are ultimately not convicted. Unlike any other
agency in our survey, the MSP provided documentation that their audit regime was functional.
e MSP system uses Cognitec and NEC face recognition algorithms.
Sources and Notes: Michigan State Police, GAO, U.S. Census MCL 28.243.2 (Last updated: September 2016). Scorecard
methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at
www.perpetuallineup.org.
MICHIGAN STATE POLICE (MSP)
JURISDICTION
Police can run face recognition searches of 41 million
Michigan driver’s license and ID photos, 4 million mug
shots, and FBI’s database of 24.9 million mug shots.
e FBI can request searches of at least 35.6 million
Michigan license photos and mug shots.
Police need probable cause to run a search on mobile
devices, but just a “law enforcement reason” for
running desktop searches.
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e Minnesota Department of Public Safety’s (DPS) Bureau of Criminal Apprehension (BCA) purchased a PictureLink system from
Dynamic Imaging to run face recognition searches of the Minnesota Repository of Arrest Photos (MRAP), primarily to generate
mug shot photo lineups for criminal investigations (016860). Police do not need reasonable suspicion to run a search. DPS stated it
does not currently use the PictureLink system’s face recognition, however, because it “does not function in a satisfactory manner”
(012103). African Americans are likely overrepresented in MRAP database; they are arrested at a rate 354% higher than their share
of the state population.
Driver and Vehicles Services (DVS), a division of the DPS, also operates a face recognition system for de-duplicating driver’s license
photos and identifying possible fraud (008924). DPS stated that this system cannot be accessed by agencies outside the DVS division
(012103).
DPS uses a Dynamic Imaging face recognition system, but it is unclear which algorithm the system employs.
Sources and Notes: DPS, GAO, U.S. Census (Last updated: August 2016). Scorecard methodology can be found in the report on
page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
MINNESOTA DEPARTMENT OF
PUBLIC SAFETY (DPS)
JURISDICTION
Police can run face recognition searches on mug shots.
In June 2016, GAO reported that the FBI was
negotiating to search Minnesota license photos. GAO
retracted that claim at the FBI’s request.
Police are not required to have reasonable suspicion to
run a search.
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In 2014, the Nebraska State Police (NSP) signed a Memorandum of Understanding (MOU) with the Nebraska Department of Motor
Vehicles (DMV). is grants NSP use of the DMV’s face recognition system, which searches 8 million driver’s license and ID photos
(009190). e FBI can also request searches of Nebraska’s driver’s license photos (GAO).
In accordance with the MOU, NSP can run searches only for criminal identity the investigations. System access is restricted to four
analysts (009190). e NSP follows procedures dictated by the DMV, and does not have its own policies, procedures, or manuals.
NSP does not conduct audits; according to the NSP, the DMV is responsible for conducting any audits of the system (009181).
In 2012, NSP also signed an MOU with the FBI for access to its pilot face recognition database, the Interstate Photo System Facial
Recognition Pilot (IPSFRP) (009183). is allowed NSP to search FBI’s database of 24.9 million mug shots submitted by various
state and federal agencies.
e DMV system uses a MorphoTrust face recognition algorithm (011954).
Sources and Notes: Nebraska State Police, GAO (Last updated: August 2016). Scorecard methodology can be found in the report
on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
NEBRASKA STATE PATROL (NSP)
JURISDICTION
Police can run face recognition searches of 8 million
Nebraska driver’s license and ID photos.
e FBI can search 8 million driver’s license and ID
photos.
Police can run searches only to assist with identity
the investigations.
System access by police is restricted to four analysts.
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In 2013, the Lincoln Police Department (LPD) signed a Memorandum of Understanding (MOU) with the Nebraska Department of
Motor Vehicles (DMV) (009175). is grants LPD use, for a four-year term, of the DMV’s face recognition system, which searches
8 million driver’s license and ID photos. Because LPD does not have its own face recognition system, it is unlikely it can search mug
shots.
e MOU permits LPD to use face recognition for identity the investigations and “for investigation of criminal activity” more
generally, unlike the MOU between the Nebraska State Patrol and the DMV, which limits police searches to identity-related crimes
only (009175, 009190). LPD does not need reasonable suspicion to run a search; the MOU states searches may be run “for a case
being investigated and/or prosecuted in a criminal manner” (009175).
In response to our public records request, LPD stated it has a use policy, but claimed it is exempt from disclosure under Neb. Rev.
Stat. 84-712.05(5) (009171).
e DMV system uses a MorphoTrust face recognition algorithm (011947).
Sources and Notes: Lincoln Police Department (Last updated: September 2016). Scorecard methodology can be found in the report
on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
LINCOLN POLICE DEPARTMENT
(LPD)
JURISDICTION
Police can run face recognition searches of 8 million
Nebraska driver’s license and ID photos.
Police are not required to have reasonable suspicion to
run a search.
LPD stated it has a use policy but claimed it is exempt
from disclosure under Nebraska’s public records law.
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e New Mexico Department of Public Safety can search 24.9 million mug shots in the FBI’s Next Generation Identication
Interstate Photo System (NGI-IPS) database (GAO). is database contains mug shots and corresponding ngerprint records
submitted by various state and federal agencies.
e FBI can also request searches of New Mexico’s 2.9 million driver’s license photos (GAO). A 2014 DOJ-funded publication by
the Police Executive Research Forum reported that police can run face recognition searches on the driver’s license photo database as
well, although we have not been able to independently verify that. e New Mexico Department of Public Safety never responded to
our public records request, led in January 2016.
Sources and Notes: GAO (Last updated: August 2016). Scorecard methodology can be found in the report on page 93. Numerical
citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
NEW MEXICO DEPARTMENT OF
PUBLIC SAFETY (DPS)
JURISDICTION
Police can search 24.9 million mug shots in the FBI’s
database.
e FBI can search New Mexico’s 2.9 million driver’s
license and ID photos.
e Department of Public Safety never responded to
our records request.
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e Albuquerque Police Department (APD) can search a mug shot database of more than 200,000 APD booking photos. e APD
uses a Universal Facial Workstation (UFW) soware platform provided by the FBI, which uses a Morpho algorithm (009202).
e APD face recognition procedural order, which the Department has not made public, states: “An ocer shall have probable
cause for an arrest (to include charges other than Concealing Identity) prior to submitting an image for Facial Recognition unless
voluntary consent is obtained” (009202). Only authorized personnel of the Real Time Crime Center are allowed to run face
recognition searches. Possible matches are considered investigative leads only and cannot be the sole basis for an arrest or detention
(009203). e procedural order also states that when using face recognition, ocers must adhere to department policies on
improper or racial proling (009203).
It is unclear if African Americans are overrepresented in the face recognition database; recent arrest data that is disaggregated
by race does not appear to be available for Albuquerque. It’s also unclear if APD “scrubs” its database to eliminate people who
were never charged, had charges dropped or dismissed, or were found innocent. APD does not appear to conduct audits of face
recognition use; in response to our request for documentation of audits, APD stated that it “does not exist.” (016700).
Sources and Notes: APD, KRQE News 13 (Last updated: September2016). Scorecard methodology can be found in the report on
page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
ALBUQUERQUE POLICE
DEPARTMENT (APD)
JURISDICTION
Police can search 200,000 APD mug shots.
Police need probable cause or consent to run a search.
It appears that APD does not audit its face recognition
system for misuse or abuse.
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Ohio began a face recognition pilot program in 2008; its current statewide system went live in 2013 (015278, 015285). e system
was almost entirely unknown to the public until a 2013 investigation by the Cincinnati Enquirer, which uncovered that the Bureau
of Criminal Investigation (BCI) had enrolled all state driver’s license photos with no notice to the public. Ocers can also run
searches against mug shot photos (015285). It is unclear if the mug shot database is “scrubbed” to eliminate people who were never
charged, had charges dropped or dismissed, or who were found innocent. e Bureau of Criminal Investigation conrmed that the
FBI—along with other law enforcement agents—may also access Ohio’s database “based upon a stated need” (016842). When the
system went live, it was reported that 30,000 police ocers and court employees could access the system. In its rst eight months of
operation, 504 dierent agencies conducted 6,618 searches (015285). At present, 4,406 individuals can access the system, and in the
rst four months of 2016, 1,429 searches were conducted by 104 agencies. BCI stated that it regularly conducts audits of the agencies
that use face recognition (016843).
Police are not required to have reasonable suspicion to run a search, but are prohibited from using face recognition to “conduct
dragnet screening of individuals” or to conduct surveillance based on constitutionally protected activity “unless doing so furthers an
ocial law enforcement activity” (009218). Ohio’s use policy has not been made public, though the Attorney General did establish
an Advisory Board with community and civil rights group representatives to provide input on face recognition use and concerns
(016843).
It appears that Ohio’s system uses a 3M Cogent face recognition algorithm (009222).
Sources and Noters: Attorney General’s Oce BCI, Ohio Rev. Code Ann. § 4501.27 (Last updated: September 2016). Scorecard
methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at
www.perpetuallineup.org.
OHIO BUREAU OF CRIMINAL
INVESTIGATION (BCI)
JURISDICTION
Police can search 24 million driver’s license and ID
photos and mug shots.
FBI ocials may be granted access to search 24 million
driver’s license photos and mug shots.
Police are not required to have reasonable suspicion to
run a search.
Cincinnati Police stated it does not use face
recognition.
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e Pennsylvania Justice Network (JNET) manages the face recognition system used by police throughout Pennsylvania. e
system is owned by the Pennsylvania Chiefs of Police Association; as a non-prot entity, however, it is not subject to Pennsylvania's
public record law, potentially making much of the information about face recognition use by police unavailable to the public. e
system launched in 2006. In 2013, it gained access to the state driver’s license photo database (016734). In April 2014, the system
could search over 34 million driver’s license and other ID photos, and over four million mug shots (010750). e system is open
“to any municipal, county, state or federal law enforcement agency” in the state; over 500 agencies use it (013787, 013785). JNET
has not made its policy face recognition use available to the public. Its internal manual does not indicate if reasonable suspicion is
required to run a search, but permits searches of witnesses (010845). JNET has stated that it conducts triennial audits of agencies
with access to its system, but provided no records of such audits in response to our request (016857, 010955–010956).
e system has been used for public surveillance. In Cheltenham Township, “[o]cers took photos of the attendees in the parking
lot” at the court hearing of an alleged gang member, and used face recognition to identify “other gang members” (016738).
African Americans are likely overrepresented in the database; they are arrested at a rate 206% higher than their share of the state
population.
e JNET system uses Cognitec, NEC, and MorphoTrust algorithms.
Sources and Notes: JNET, GAO, Pennsylvania State Police, Pennsylvania Uniform Crime Reporting System, U.S. Census (Last
updated: September 2016). Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to
ocial records available online at www.perpetuallineup.org.
PENNSYLVANIA JUSTICE
NETWORK (JNET)
JURISDICTION
Police can run face recognition searches of over 34
million driver’s license and ID photos and over 4
million mug shots.
In June 2016, GAO reported that the FBI was
negotiating to search state license photos. GAO
retracted that claim at the FBI’s request.
It’s unclear if police are required to have reasonable
suspicion to run a search.
Police from over 500 agencies use the system.
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e Texas Department of Public Safety (DPS) purchased a face recognition system in 2005, following the passage of a law
requiring the DPS to establish an image verication system for driver’s licenses (009310, Tex. Code Ann. § 521.059). is system
searches 24 million driver’s license and ID photos. DPS can also search 24.9 million mug shots in the FBI’s database, the Next
Generation Identication Interstate Photos System, or NGI-IPS (GAO). e FBI’s FACE Services unit can request searches of
the state’s 24 million driver’s license and ID photos (GAO).
Texas state law requires the DPS face recognition system to be used to aid law enforcement agencies in “conducting an investigation
of criminal conduct” (Tex. Code Ann. § 521.059). It is unclear if law enforcement agencies need reasonable suspicion to run a
search. In response to our public records request, DPS did not provide a policy specically regarding the use of face recognition
technology.
In February 2016, Dallas Area Rapid Transit (DART) announced plans to deploy real-time face recognition. In response to our
public records request, DART indicated that procurement is in its early stages, and no responsive records exist yet (011102). e
Dallas Police Department, El Paso Police Department, San Antonio Police Department, and Fort Worth Police Department
reported that they do not use face recognition technology.
e initial DPS face recognition system was purchased from Digimarc; it is unclear which company provides the current system or
algorithm (009310).
Sources and Notes: Texas DPS, Tex. Code Ann. § 521.059, GAO, Dallas Area Rapid Transit, Fort Worth Police Department (Last
updated: September 2016) Scorecard methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to
ocial records available online at www.perpetuallineup.org.
TEXAS DEPARTMENT OF PUBLIC
SAFETY (DPS)
JURISDICTION
Police can search 24.9 million mug shots in FBI’s
database and 24 million driver’s license and ID photos.
It is unclear if police are required to have reasonable
suspicion to run a search.
FBI can search 24 million driver's license and ID
photos.
Dallas Transit Police are planning to acquire real-time
face recognition in late 2016.
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In response to our public records request, Vermont State Police has stated it does not use face recognition technology (013790).
However, according to a 2016 report by the Government Accountability Oce, the FBI’s FACE Services unit can request face
recognition searches of Vermont’s 1.8 million driver’s license and ID photos (GAO). Since the FBI submits searches to be run
by the responsible state agency, this would appear to indicate that the Vermont Department of Motor Vehicles (DMV) has
implemented a face recognition system that searches state driver’s license photos (GAO).
e DMV face recognition system, and its use by the FBI, may conict with Vermont state law, which states that the DMV “shall
not implement any procedures or processes for identifying applicants for licenses . . . that involve the use of biometric identiers.”
(Vt. Stat. Ann. Tit. 23, § 634).
Sources and Notes: GAO, Vermont State Police, Vt. Stat. Ann. Tit. 23, § 634. (Last updated: August 2016) Scorecard methodology
can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at
www.perpetuallineup.org.
VERMONT
JURISDICTION
FBI can search 1.8 million Vermont driver's license
and ID photos.
FBI searches may conict with a state law that
prohibits the use of biometric identication on DMV
records.
Vermont State Police stated it does not use face
recognition.
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e Virginia State Police (VSP) can run face recognition searches of 1.2 million Virginia mug shots using the VSP’s Centralized
Criminal Information System (CCIS) (015274). Mug shots are “retained indenitely,” which means VSP does not “scrub” its
database to eliminate people who had charges dropped or dismissed except when records are expunged (015264, 016858). “Each
criminal justice agency in Virginia” can access the system upon request (015303). Police are not required to have reasonable
suspicion to run a search; use of the system is limited to “criminal justice purposes only” (015272). VSP does not conduct audits of
how the system is used (015264, 016859).
African Americans are likely overrepresented in the system; statewide, they are arrested at a rate 108% higher than their share of
Virginia’s population.
VSP uses a DataWorks Plus face recognition system, which employs a NEC algorithm (016858).
Sources and Notes: VSP, U.S. Census (Last updated: September 2016) Scorecard methodology can be found in the report on
page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
VIRGINIA STATE POLICE (VSP)
JURISDICTION
Police can search 1.2 million mug shots.
Police are not required to have reasonable suspicion to
run a search.
e system can be accessed by all law enforcement
agencies in the state on request; audits are not
conducted.
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e Northern Virginia Regional Information System (NOVARIS), located in Fairfax County, is a regional arrest data and
evidence sharing system. NOVARIS’s face recognition component was acquired in 2007 and can be accessed by law enforcement
in Maryland’s Prince George’s and Montgomery counties, Virginia’s Arlington, Fairfax, Loudoun, and Prince William counties,
and the cities of Alexandria, Fairfax, and Falls Church (015230). NOVARIS is also part of a face recognition partnership system
established by the Pinellas County Sheri ’s Oce in Florida (015214). It is unclear how many mug shots are enrolled in the
database, or if it is “scrubbed” to eliminate people who were never charged, had charges dropped or dismissed, or who were found
innocent.
In its response to our public records request, Fairfax County Police indicated: “Only trained examiners who are certied
and authorized to operate AFIS searches shall initiate cross-database searches within the AFIS databases,” but there is no
corresponding “certied examiner” requirement for access to face recognition (014710). is suggests that the required training and
certication threshold is lower for ocers running a face recognition search than for those running a ngerprint search. It is unclear
if reasonable suspicion is required for police to run a search.
Sources and Notes: Fairfax County Police Department (Last updated: September 2016). Scorecard methodology can be found in the
report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
NORTHERN VIRGINIA REGIONAL
INFORMATION SYSTEM (NOVARIS)
JURISDICTION
Police can search mug shots.
It’s unclear if police are required to have reasonable
suspicion to run a search.
Certied “examiner” status is required for ngerprint
searches but not for face recognition searches.
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e Seattle Police Department runs its Booking Photo Comparison Software (BPCS) face recognition system through South
Sound 911, an intergovernmental agency. Launched in 2014, it contains mug shots from Snohomish, King, and Pierce Counties,
doesn’t search driver’s license and ID photos, and is accessible to at least eight regional law enforcement agencies (009378, 009826,
011900). e SPD Manual section on face recognition is publicly available online and requires reasonable suspicion to run a search.
It does not allow searches of bystanders or witnesses (009907). e Seattle City Council conditioned funding for the system on the
policy’s review and approval by the ACLU of Washington (012666).
When South Sound 911 solicited face recognition vendors, they expressed a preference for a system with “the ability to do face
recognition searches against live-feed video” (012048). A 2013 contract and invoices show that such a system was likely purchased
(009790, 011078). e Seattle Police Department subsequently acquired the contract and ownership of the system, and has since
prohibited the use of real-time video-based face recognition. (011078, 011082).
An FAQ for the system says that it “does not see race, sex, orientation or age” (009377). is contradicts a 2012 FBI co-authored
study, and does not reect the fact that African Americans are likely overrepresented in the system. In King County, for example,
they are arrested at a rate 294% higher than their share of the population.
e BPCS system uses an NEC face recognition algorithm (012067).
Sources and Notes: SPD, South Sound 911, King County, IEEE, U.S. Census (Last updated: September 2016). Scorecard
methodology can be found in the report on page 93. Numerical citations, e.g. (123456), refer to ocial records available online at
www.perpetuallineup.org.
SEATTLE POLICE DEPARTMENT
(SPD) & SOUTH SOUND 911
JURISDICTION
Police can run face recognition searches of mug shots.
Police are required to have reasonable suspicion to run
a search.
South Sound 911 requested real-time capabilities when
contracting, but the Seattle PD has since banned such
use.
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e West Virginia Intelligence Fusion Center (WVIFC) is operated by the state of West Virginia and runs advanced face
recognition that is part of a system “open to all federal, state, county, and local agencies” (009944).
e system searches mug shot photos, but it’s unclear what other databases are accessible, how many mug shots are enrolled in
the system, and if the WVIFC “scrubs” its mug shot database to eliminate people who were never charged, had charges dropped
or dismissed, or who were found innocent. e WVIFC has no publicly available policy governing law enforcement use of face
recognition. e internal “Facial Recognition System Policy” is a page and a half long and does not require ocers to have a
reasonable suspicion before running a search (009959–009960).
In 2012, the WVIFC bought a real-time face recognition system that “[a]utomatically monitor[s] video surveillance footage
and other video for instances of persons of interest” (009966). Previously, the WVIFC had told state ocials that “automated
video processing, search and detection capabilities could provide dramatic payos,” and that “WVIFC requires a system with the
following minimum capabilities: . . . Ability to automatically detect and extract faces from video” (009971–009972).
“Everyone refers to the Minority Report . . . about how they use facial recognition and iris recognition,” said omas Kirk, the
director of the WVIFC, in a recent interview. He added: “I actually think that that is the way of the future.”
e WVIFC system was purchased from Tygart Technologies, but it is unclear which company provides the face recognition
algorithm used.
Sources and Notes: WVIFC, Tygart, Vocativ (Last updated: August 2016). Scorecard methodology can be found in the report on
page 93. Numerical citations, e.g. (123456), refer to ocial records available online at www.perpetuallineup.org.
WEST VIRGINIA INTELLIGENCE
FUSION CENTER (WVIFC)
JURISDICTION
e WVIFC bought real-time face recognition
capabilities in 2012.
Federal, state and local law enforcement ocers can
run face recognition searches of mug shots.
It’s unclear if ocers are required to have reasonable
suspicion to run a search.
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