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. Then, in 2014, a State Department official 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

The 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. The 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.

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 finally brought to justice through the last-resort use of face recognition by a sophisticated federal law enforcement agency.59 The 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. The Pinellas County Sheriff’s Office in Florida, for example, began implementing its current system in 2001.61 Over 5,300 officials from 242 federal, state, and local agencies have access to the system.62 In Pennsylvania, officials 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 field offices have direct access to the Pinellas County Sheriff’s Office 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 field offices can access other state systems, such as those in Pennsylvania and Maryland.66

The Department of Defense, the Drug Enforcement Administration, Immigrations and Customs Enforcement, the Internal Revenue Service, the Social Security Administration, the U.S. Air Force Office 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. The remainder was run against the Department of Defense database and the Department of State’s visa and passport photo databases.69

In its first 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 first four months of 2016, the system was searched 1,429 times by 104 different agencies.70 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.72 This 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 effective these searches are. There 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 identifications resulted from those potential matches. The 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.

I. Moderate Risk Deployments

Of the 52 agencies we surveyed which were now using or had previously used or obtained face recognition technology, we identified 29 that are deploying face recognition under a Moderate Risk model—Stop and Identify, Arrest and Identify, and/or Investigate and Identify off 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 The norm, rather, is reflected in an agency like the Pinellas County Sheriff’s Office. 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

II. High Risk Deployments

High risk deployments—whether Stop and Identify, Arrest and Identify, or Investigate and Identify—are typified 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 verified against the GAO report or through our own research, we identified 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 The U.S. Census estimated that in 2014, there were 245,273,438 American adults in the country.80 This means that, at a minimum, roughly 1 in 2 American adults (48%) have had their photos enrolled in a criminal face recognition network.

The figure is likely larger than that. In 2013, the Washington Post and the Cincinnati Enquirer conducted similar surveys that flagged 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 That comes out to 53% of the adult population.83

Drivers and Adults in Law Enforcement Face Recognition Networks (2014)
Figure 6 :  Drivers and Adults in Law Enforcement Face Recognition Networks (2014)

Note: States and agencies reported to allow or run searches of license and ID photos are in italics.84This is not an exhaustive accounting of law enforcement access to driver’s license photo databases. Other states may allow this access that were not identified in our research.

Sources: GAO, FOIA documents, U.S. Dep’t of Transportation, Federal Highway Administration, Washington Post, Cincinnati Enquirer, Police Executive Research Forum

III. 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. The company, called N-Tech.Lab, would test their software on footage from Moscow’s CCTV cameras. “People who pass by the cameras are verified 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 officer near the location.” Following the trial, the company will reportedly install its software on Moscow’s CCTV system. The city has over 100,000 surveillance cameras.86

Is real-time video surveillance like that seen in Moscow coming to major American cities? The 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 “configured 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: “The system should have the ability to do facial recognition searches against live-feed video.”"89 However, the final 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
  • The Dallas Area Rapid Transit police announced plans to deploy real-time face recognition software throughout its system sometime in 2016.91

This means that five 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.

The supply exists to meet this demand. Almost all major face recognition companies advertise real-time face recognition systems. Specifically:

  • NEC, the top performer in NIST accuracy tests,92 advertises that “[f]ace recognition can do far more than is generally understood,” and offers an “application for real-time video surveillance” that can “[d]etect[] subjects in a crowd in real time.”93
  • 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 Identification System” that “uses live video to match identities in real time . . . The system automatically recognizes multiple faces . . . to identify individual people from dynamic, uncontrolled environments.”95
  • Safran Identity & Security offers 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
Excerpts from an NEC brochure for the NeoFace Watch real-time face recognition system.
Figure 7 :  Excerpts from an NEC brochure for the NeoFace Watch real-time face recognition system.

The 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 off 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, Thomas 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 effectiveness and sharply limit its accuracy. NIST is currently running the first 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. The first focuses on the main differentiator between moderate and high risk systems—the people enrolled in the system’s face recognition database. The 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 offense, and/or with mug shots affirmatively “scrubbed” by police to eliminate no-charge arrests or not-guilty verdicts.
  • 0 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. 
  • 0 No written policy addressing real-time or historical video surveillance, but agency has affirmatively 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 affirmatively disclaimed these practices.

Next Section
  • 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 Sheriff’s Office stated that the suspect’s identity was confirmed through the use of PCSO’s face recognition program. Pinellas County Sheriff's Office, Email from Jake Ruberto, Technical Support Specialist to Clare Garvie (Jul. 13, 2016) (on file 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. The 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, Office 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, fulfilling training, or signing an agreement with an agency that has a face recognition system.
  • 61. See Pinellas County Sheriff’s Office, 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 Sheriff’s Office, 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 Sheriff's Office, Interview with PCSO Sheriff Bob Gualtieri and Technical Support Specialist Jake Ruberto (July 26, 2016) (indicating that 242 agencies at the federal, state, and local level have access. Notes on file 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 officer, deputy, investigator or crime analyst in LA County” is permitted to access the Los Angeles County Sheriff Department’s system. Los Angeles County Sheriff’s Department, Facial Identification 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 Office, GAO-16-267, Face Recognition Technology: FBI Should Better Ensure Privacy and Accuracy 51 (May 2016).
  • 65. Pinellas County Sheriff's Office, Interview with PCSO Sheriff Bob Gualtieri and Technical Support Specialist Jake Ruberto (July 26, 2016).
  • 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 officers 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. The Arizona Counterterrorism Information Center, run by the Maricopa County Sheriff’s Office, has conducted face recognition searches for federal investigations since 2008. Maricopa County Sheriff’s Office, Letter from Deputy Chief Ray Churay to Deputy Chief David Hendershott (Apr. 21, 2008), Document p. 015070. The Pinellas County Sheriff’s Office has signed MOUs with the U.S. Air Force Office of Special Investigations, the IRS Criminal Investigation Field Office in Tampa, the Social Security Administration, and four other federal agencies with Florida branches. Memoranda of Understanding between Pinellas County Sheriff’s Office and U.S. Air Force Office of Special Investigations Detachment 340 (Jan. 16, 2015), Document p. 013798; Department of Agriculture and Consumer Services, Office 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 Office (Aug. 20, 2013), Document p. 014125; 6th Security Forces Squadron (Jan 21, 2009), Document p. 014208; Social Security Administration, Office of the Inspector General, Office of Investigations (Dec. 16, 2014), Document p. 014594; U.S. Department of Veterans Affairs 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 Sheriff’s Office, 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 Office, 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 Sheriff’s Office, Florida's Facial Recognition Network, FACES Training 2015, Document p. 014396.
  • 73. The 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 Office, GAO-16-267, Face Recognition Technology: FBI Should Better Ensure Privacy and Accuracy 49 (May 2016).
  • 74. See U.S. Gov’t Accountability Office, 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. This is mandated by state law. See Mich. Comp. Laws Ann § 28.243. Additionally, members of the Digital Analysis and Identification 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 Identification Section (May 25, 2016) (notes on file with authors).
  • 77. Pinellas County Sheriff's Office, Interview with PCSO Sheriff Bob Gualtieri and Technical Support Specialist Jake Ruberto (July 26, 2016) (notes on file with authors). There is currently a five-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.fl.us/cms/Seal-and-Expunge-Process/Seal-and-Expunge-home.aspx (last visited Sept. 25, 2016).
  • 78. See U.S. Gov’t Accountability Office, GAO-16-267, Face Recognition Technology: FBI Should Better Ensure Privacy and Accuracy 51(map). In 2013, the Washington Post and the Cincinnati Enquirer identified 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 file with authors); Chrissie Thompson 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 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://factfinder.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. These 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://factfinder.census.gov/bkmk/table/1.0/en/PEP/2014/PEPAGESEX.
  • 84. States or agencies are identified in italics if they were reported to allow or have access to these searches, but we were unable to verify their current access status.
  • 86. Daniil Turovsky, The 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 justification 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. The grant narrative also detailed the Department’s plan to purchase video processing software that “will efficiently 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. The 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 file 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/. The DART General Counsel’s office was unable to locate records in response to our request for information on this system; however, officials confirmed in a phone interview that negotiations were underway to secure funding for the system. Interview with DART General Counsel’s Office (notes on file with authors).
  • 92. Patrick Grother and Mei Ngan, Face Recognition Vendor Test: Performance of Face Identification 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 Identification System Takes Security Solutions from Reactive to Proactive (Sept. 20, 2016), http://www.businesswire.com/news/home/20160920006378/en/3M-Live-Face-Identification-System-Takes-Security.
  • 95. FaceVACS-VideoScan, Cognitec Systems (June 2016), http://www.cognitec.com/files/layout/downloads/FaceVACS-VideoScan-5-3-flyer.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 Thinks 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/five.cfm (last visited Sept. 23, 2016).