{"id":59009,"date":"2020-10-01T08:00:06","date_gmt":"2020-10-01T12:00:06","guid":{"rendered":"https:\/\/www.policechiefmagazine.org\/?p=59009"},"modified":"2025-02-12T11:31:30","modified_gmt":"2025-02-12T16:31:30","slug":"getting-in-front-of-facial-recognition","status":"publish","type":"post","link":"https:\/\/www.policechiefmagazine.org\/getting-in-front-of-facial-recognition\/","title":{"rendered":"Getting in Front of Facial Recognition"},"content":{"rendered":"<p> <\/p>\n<p>It should come as no surprise that facial recognition software is seen by many law enforcement officials as a valuable tool to help identify or locate people, either to assist victims or bring offenders to justice. Anecdotal evidence suggests that this technology, when used in concert with other available investigative resources and processes, can significantly help resolve criminal cases and increase public safety.<\/p>\n<p>However, with its growing use by law enforcement comes a substantial rise in scrutiny from myriad sources, including the media, privacy advocates, and public officials. The resulting criticism has the potential to influence public sentiment in a negative way, jeopardizing a broader use of this powerful technology. In addition, new concerns are being raised about the accuracy of facial recognition when a subject\u2019s face is partially covered, such as when a person is wearing a mask to avoid spreading a virus like COVID-19. Studies on that particular use case are just beginning, so it is too early to determine the true impact mask-wearing will have on facial recognition accuracy. However, it is important for law enforcement officials to stay up to date with all aspects of this constantly evolving technology\u2014and to never rely solely on it. The images provided by facial recognition systems are for lead generation purposes only; the images should not be considered as positive identification or be used as the lone basis for any law enforcement action.<\/p>\n<p>Understanding the nature of prevailing public perceptions of facial recognition technology and the underlying reasons for them is especially important so police leaders can make informed decisions regarding the use of facial recognition technology, both now and in the future.<\/p>\n<h2><strong><mark>The Criticism<\/mark><\/strong><\/h2>\n<p>The use of facial recognition technology by police is quite often met with condemnation. Police executives using or considering the use of facial recognition need to be aware of the evolving political and social landscape under which these technologies are being deployed and the potential impact on public perception. The examples presented herein stem from multiple arenas and demonstrate salient critical themes.<\/p>\n<h3>Academia<\/h3>\n<p>In 2016, a report titled <em>The Perpetual Line-up: Unregulated Police Face Recognition in America<\/em> was published by the Georgetown Law Center on Privacy and Technology.<sup>1<\/sup> While much of the report is critical, it does acknowledge the benefits of facial recognition in the apprehension of criminals:<\/p>\n<p style=\"padding-left: 40px;\"><em>The benefits of face recognition are real. It has been used to catch violent criminals and fugitives. The law enforcement officers who use the technology are men and women of good faith. They do not want to invade our privacy or create a police state. They are simply using every tool available to protect the people that they are sworn to serve. Police use of face recognition is inevitable. This report does not aim to stop it.<\/em><sup>2<\/sup><\/p>\n<p>Despite this acknowledgement, a highlight of the report\u2019s conclusions, as outlined below, suggests a more critical tone:<\/p>\n<ul>\n<li>One in two American adults\u2019 images are in a law enforcement face recognition network.<\/li>\n<li>Law enforcement face recognition networks include images of more than 117 million American adults.<\/li>\n<li>Law enforcement face recognition is unregulated and, in many instances, out of control.<\/li>\n<li>Law enforcement agencies are not taking adequate steps to protect free speech.<\/li>\n<li>Most law enforcement agencies do little to ensure their systems are accurate.<\/li>\n<li>The human backstop to accuracy is non-standardized and overstated.<\/li>\n<li>Police use of face recognition will disproportionately affect African Americans.<\/li>\n<li>Agencies are keeping critical information from the public.<\/li>\n<li>Major face recognition systems are not audited for misuse.<sup>3<\/sup><\/li>\n<\/ul>\n<h3>Media<\/h3>\n<p>Since 2016, numerous media reports have criticized the use of facial recognition systems by law enforcement. Among the more noteworthy and recent articles are the following three from the <em>Wall Street Journal<\/em>:<\/p>\n<h4><em>\u201cShould Government Halt the Use of Facial-Recognition Technology?\u201d (February 2020)<\/em><\/h4>\n<p>Critics say false positives are far higher for some groups of people than for others, with misidentification most common for Asian Americans and African Americans, but supporters of the technology say the actual numbers involved are tiny.<sup>4<\/sup><\/p>\n<h4><em>\u201cTech Firms Seek to Head Off Bans on Facial Recognition\u201d (March 2020)<\/em><\/h4>\n<p>Technology companies such as Microsoft and Amazon are encouraging legislation to restrict use of facial recognition systems in an effort to head off the outright bans that some cities and states are considering.<sup>5<\/sup><\/p>\n<h4><em>\u201cACLU Sues U.S. for Records of Facial-Recognition Use at Airports\u201d (March 2020)<\/em><\/h4>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-59013\" src=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-173245405-mod-300x200.jpg\" alt=\"\" width=\"400\" height=\"266\" srcset=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-173245405-mod-300x200.jpg 300w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-173245405-mod-768x511.jpg 768w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-173245405-mod-272x182.jpg 272w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-173245405-mod.jpg 890w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/>The American Civil Liberties Union is suing U.S. federal agencies for records about the use of facial recognition at airports and other places where travelers enter the United States, the latest salvo in an activist campaign to halt use of the technology. The suit, filed in U.S. District Court for the Southern District of New York, seeks to compel the U.S. Department of Homeland Security and other government agencies to release documents regarding the policy\u2019s use, including government contracts with airports and airlines. The ACLU originally asked for the records in January 2020 under the Freedom of Information Act.<sup>6<\/sup><\/p>\n<h3>Legislative<\/h3>\n<p>In addition to a plethora of media articles and Internet postings, measures to limit the use of facial recognition systems by law enforcement have been either proposed or passed by state and federal legislative representatives. Three of the more recent measures (as of the writing of this article) are by Washington state, the U.S. Senate, and the UK Judiciary:<\/p>\n<h4><em>Washington State Senate Bill<\/em><\/h4>\n<p>In February 2020, the Washington Senate passed a bill to regulate the governments\u2019 use of facial recognition technology, prohibiting the use of the technology for ongoing surveillance without a search warrant and requiring any decision based on facial recognition with a legal impact (e.g., health care, housing, employment) to be reviewed by an in-house trained facial recognition lead.<sup>7<\/sup><\/p>\n<h4><em>U.S. Senate Bills<\/em><\/h4>\n<p>In November 2019, a bill titled The Facial Recognition Technology Warrant Act of 2019 was introduced in the U.S. Senate by Senators Chris Coons (D-DE) and Mike Lee (R-UT). According to Coons, the bill is designed to \u201cmake sure that law enforcement has the tools necessary to keep us safe while also protecting fundamental Fourth Amendment privacy rights.\u201d<\/p>\n<p>If passed, the bill would apply only to federal law enforcement agencies, who would be required to get a warrant in order to conduct facial recognition surveillance of an individual for more than 72 hours. Surveillance authorized by a warrant is limited to a maximum of 30 days, and decisions to issue or deny warrants must be reported.<sup>8<\/sup><\/p>\n<p>More recently, in February 2020, a U.S. Senate bill was introduced by Senators Jeff Merkley (D-OR) and Cory Booker (D-NJ) to temporarily ban law enforcement\u2019s use of facial recognition. The bill would prohibit police use of the technology until guidelines and limitations are set by a commission.<sup>9<\/sup><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-medium wp-image-59020\" src=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-1155570204-com-207x300.jpg\" alt=\"\" width=\"207\" height=\"300\" srcset=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-1155570204-com-207x300.jpg 207w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-1155570204-com.jpg 527w\" sizes=\"auto, (max-width: 207px) 100vw, 207px\" \/><\/p>\n<p>The bill cites some of the concerns legislators have about the use of facial recognition technology, including inaccuracy and potential perceived suppression of civil liberties.<\/p>\n<p style=\"padding-left: 40px;\"><em>Congress finds the following:<\/em><\/p>\n<p style=\"padding-left: 80px;\"><em>(1) Facial recognition is a technology that is increasingly being used and marketed to law enforcement agencies across the United States without appropriate debate or consideration of its impacts.<\/em><\/p>\n<p style=\"padding-left: 80px;\"><em>(2) Facial recognition has been shown to disproportionately impact communities of color, activists, immigrants, and other groups that are often already unjustly targeted.<\/em><\/p>\n<p style=\"padding-left: 80px;\"><em>(3) Facial recognition has a history of being inaccurate, particularly for women, young people, African Americans, and other ethnic groups.<\/em><\/p>\n<p style=\"padding-left: 80px;\"><em>(4) There is evidence that facial recognition has been used at protests and rallies, which could chill speech.<\/em><\/p>\n<p style=\"padding-left: 80px;\"><em>(5) It is critical that facial recognition not be used to suppress First Amendment related activities, violate privacy, or otherwise adversely impact individuals\u2019 civil rights and civil liberties.<\/em><sup>10<\/sup><\/p>\n<h4><em>UK Court Ruling<\/em><\/h4>\n<p>In a landmark decision, a British court found that the South Wales Police\u2019s use of facial recognition to hunt for suspects \u201chad breached privacy, data protection, and equality regulations.\u201d<\/p>\n<p>Although the August 2020 decision does not ban the use of facial recognition in the UK, privacy advocates considered it a victory. \u201cThe court has agreed that this dystopian surveillance tool violates our rights and threatens our liberties,\u201d said Megan Goulding, a lawyer for civil rights group Liberty.<sup>11<\/sup><\/p>\n<h4><em>U.S. State Bans<\/em><\/h4>\n<p>The backlash against the use of facial recognition has resulted in legislative action by cities in the United States to ban the technology, including a June 2020 ban for law enforcement use in Boston, Massachusetts; a July 2020 statewide ban on its use in New York schools until 2022; and an August 2020 Portland, Oregon, ban on its use by city officials.<sup>12<\/sup><\/p>\n<p>The arguments put forth in the preceding study, media reports, and legislative actions are that facial recognition disproportionately impacts minorities or communities of color, activists, immigrants, and other groups; has a history of being inaccurate for women, young people, African Americans, and other ethnic groups; and violates rights and threatens liberties.<\/p>\n<p>However, much of this criticism and these legislative actions are now being called out as disingenuous by two of the most recent facial recognition accuracy research efforts. Both the International Biometrics and Identity Association, and the Information Technology and Innovation Foundation have shed new light on the alleged disparate impact critics say exist with these facial recognition systems.<sup>13<\/sup> Both of these organizations conducted analyses of facial recognition data that show that many of the top-rated algorithms used by a vast majority of police agencies have virtually no unintended consequences such as racial or gender bias. However, the lowest-performing algorithms among those tested did have higher error rates. In many cases, the critics of law enforcement facial recognition used National Institute of Standards and Technology (NIST) tests of the lower-performing algorithms with much higher error rates rather than the high-performing systems actually being used by police forces.<\/p>\n<h2><strong><mark>The Science<\/mark><\/strong><\/h2>\n<p>A rigorous scientific study conducted by NIST is widely regarded as the most neutral and fair dissertation on facial recognition, and law enforcement leaders should be familiar with its major findings. It can serve as a scientific baseline to help refute the more critically predisposed research by many media and watchdog groups.<\/p>\n<h3>NIST Report Highlights<\/h3>\n<p><em>Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects<\/em>, published by NIST in December 2019, evaluates the accuracy variations of facial recognition systems across demographic groups.<sup>14<\/sup> This is the third in a series of reports on ongoing FRVTs executed by NIST. The NIST report starts with acknowledgement that prior studies, e.g., the Georgetown University work, \u201carticulated sources of bias, described the potential impacts particularly in a policing context.\u201d<sup>15<\/sup> This report was motivated by studies of demographic effects in more recent face recognition and gender estimation algorithms. These algorithms, which vary among vendors, drive the results of facial recognition systems presented to the end user. The NIST tests help to quantify demographic differences in contemporary face recognition algorithms.<\/p>\n<p>The purpose of the NIST report, as stated, is<\/p>\n<p style=\"padding-left: 40px;\"><em>to inform discussion and decisions about the accuracy, utility, and limitations of face recognition technologies. Its intended audience includes policy makers, face recognition algorithm developers, systems integrators, and managers of face recognition systems concerned with mitigation of risks implied by demographic differentials.<\/em><sup>16<\/sup><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-59023 size-large\" src=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-182174370-com-1024x474.jpg\" alt=\"\" width=\"660\" height=\"306\" srcset=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-182174370-com-1024x474.jpg 1024w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-182174370-com-300x139.jpg 300w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-182174370-com-768x355.jpg 768w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/GettyImages-182174370-com.jpg 1381w\" sizes=\"auto, (max-width: 660px) 100vw, 660px\" \/>The NIST tests were extensive, using algorithms with four large datasets of photographs collected in U.S. governmental applications currently in operation including domestic mugshots, U.S. application photographs from a global population of applicants for immigration bene\ufb01ts, visa photographs submitted in support of visa applications, and border crossing photographs of travelers entering the United States. Together these datasets allowed NIST to process more than 18 million images of more than 8 million people through 189 mostly commercial algorithms from 99 developers.<\/p>\n<p>NIST assessed accuracy by demographic group and provides details on false negative and false positive effects, defining false negatives as \u201cthe failure to associate one person in two images,\u201d typically caused by a change in the person\u2019s appearance or image property issues, and false positives as \u201cthe erroneous association of samples of two persons,\u201d as can occur when the images of two people\u2019s faces have similarities.<sup>17<\/sup><\/p>\n<p>Regarding false positives, NIST found that false positive differentials are much larger than those related to false negatives and exist broadly, across many, but not all, algorithms tested. In addition, NIST determined the following about false positives:<\/p>\n<ul>\n<li>Across demographics, false positive rates often vary by factors of 10 to beyond 100 times.<\/li>\n<li>With domestic law enforcement images, the highest false positives are in American Indians, with elevated rates in African American and Asian populations; the relative ordering depends on sex and varies with algorithms.<\/li>\n<li>False positives were higher in images of women than men; this is consistent across algorithms and datasets.<\/li>\n<li>Elevated false positives existed in images of elder adults and in children.<\/li>\n<li>\u201cSome developers supplied highly accurate identi\ufb01cation algorithms for which false positive differentials are undetectable.\u201d<sup>18<\/sup><\/li>\n<\/ul>\n<p>NIST also found that the quality of the application photos affect both false negatives and positives.<\/p>\n<p>Regarding false negatives, NIST found the following:<\/p>\n<ul>\n<li>False negatives tend to be more algorithm-speci\ufb01c and vary often by factors such as race and image quality.<\/li>\n<li>False negatives are higher in Asian and American Indian individuals with error rates above those in white and African American faces (which yield the lowest false negative rates).<\/li>\n<li>In cooperative access control applications (e.g., taking a mugshot), false negatives can be remedied by users making second attempts.<sup>19<\/sup><\/li>\n<\/ul>\n<p>Among the findings, the information that can be most useful to law enforcement officials in using facial recognition systems includes the following:<\/p>\n<ul>\n<li>Image quality matters and affects error rates (false negatives and false positives).<\/li>\n<li>Different algorithms perform better or worse in processing images of individuals of various demographics.<\/li>\n<li>Policy makers and law enforcement officials should carefully research the face recognition system developers and become aware of the accuracy differences among systems.<\/li>\n<li>To mitigate false positives regarding demographic differentials, NIST suggests the use of \u201cthreshold elevation, re\ufb01ned training, more diverse training data, discovery of features with greater discriminative power\u2014particularly techniques capable of distinguishing between twins and use of face and iris as a combined modality.\u201d<\/li>\n<li>False negative error rates, and demographic differentials therein, are reduced in standards-compliant images, suggesting further research into image quality analysis, face-aware cameras, and improved standards compliance is needed.<\/li>\n<\/ul>\n<h2><strong><mark>The Response<\/mark><\/strong><\/h2>\n<p>Facial recognition is a powerful tool that will likely be in media headlines well into the foreseeable future, so complete awareness of both its strengths as a law enforcement tool and its controversial nature as a biometric technology is strongly encouraged. There are some recommended actions police executives can take to make informed decisions about facial recognition technology, especially regarding deployment, use, policy, and community transparency.<\/p>\n<h3>Know How Facial Recognition Works\u2014and How It Is Used<\/h3>\n<p>Critics and supporters alike often have misconceptions concerning the way facial recognition is deployed by law enforcement. Media reports, movie and television depictions, and private sector utilization all contribute to the confusion. In light of that, the IJIS Institute and IACP created <em>The Law Enforcement Facial Recognition Use Case Catalog<\/em> to help both practitioners and community members understand when facial recognition is utilized by police agencies and under what conditions.<sup>20<\/sup> The catalog outlines every known use of the technology by sworn law enforcement agencies and provides examples of uses and real stories of actual cases.<\/p>\n<p>Many of the 19 uses addressed in the catalog can also be performed with two distinctly different intentions:<\/p>\n<ul>\n<li>Discovery\u2014helping find one person among many persons (one-to-many comparison)<\/li>\n<li>Identification\u2014helping verify the identity of one person (one-to-one comparison)<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-59015\" src=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-FIGURE-1-USE-CASE-VARIABLES-300x111.jpg\" alt=\"\" width=\"900\" height=\"334\" srcset=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-FIGURE-1-USE-CASE-VARIABLES-300x111.jpg 300w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-FIGURE-1-USE-CASE-VARIABLES-1024x379.jpg 1024w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-FIGURE-1-USE-CASE-VARIABLES-768x285.jpg 768w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-FIGURE-1-USE-CASE-VARIABLES-1536x569.jpg 1536w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-FIGURE-1-USE-CASE-VARIABLES.jpg 1719w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/p>\n<p>The catalog provides a helpful primer on how the technology operates, and the three categories in which the two types of searches are done: emergency, investigatory, and custodial or supervisory.<\/p>\n<p>In addition to the catalog, the information published by NIST and other technical resources can be a reference source to help an agency understand the strengths and weaknesses of any facial recognition system, which is important in deciding to select or use a given facial recognition system.<\/p>\n<h3>Learn the Most Important Factors to Emphasize About Facial Recognition<\/h3>\n<p>The IACP Criminal Justice Information Systems (CJIS) Committee took the <em>Use Case Catalog<\/em> and created a simple <em>Guiding Principles for Law Enforcement Use of Facial Recognition Technology<\/em> from the information.<sup>21<\/sup> There are five important principles that are simply stated and easy to understand within the document, but all are based on the most important element regarding police use of facial recognition\u2014results are only a clue, not probable cause! This single issue is at the heart of much of the criticism of this technology, since there is a pervasive perception that once an image is selected by a facial recognition system, an arrest is soon to follow.<\/p>\n<p>Police executives would also do well by reiterating to those under their command, political leaders, technology vendors, the media, and members of the public that all final law enforcement facial recognition system decisions that suggest a match between two or more images are <em>always<\/em> confirmed by a human being. A computer makes suggestions, and a trained law enforcement examiner must emphatically agree after careful consideration before it\u2019s considered a positive identification. Even after that confirmation process, a facial recognition image selection is just a small part of the puzzle since proper investigatory procedures must always be followed, similar to all police cases.<\/p>\n<h3>Have a Written Plan and Follow It<\/h3>\n<p>Many police agencies are already operating facial recognition systems without adequate written policies or procedures or are basing their department documentation on vendor-provided system manuals and the like. If the first two sections of this article do nothing else, they should convince law enforcement leadership that professionally researched and well-written plans for procuring, implementing, operating, and explaining facial recognition are essential. The suppliers of facial recognition systems can certainly provide guidance regarding the proper operation of the technology and its performance expectations, but police agency standard operating procedures and all related documentation should be carefully created through a deliberative process with proper government stakeholders.<\/p>\n<p>The IACP CJIS Committee took one additional step by drafting policy guidelines for the use of this technology to help police leaders who are using or may be considering facial recognition systems. Through the IACP Law Enforcement Policy Center, the CJIS Committee is working with other stakeholders and reviewers to refine these guidelines, ensuring they are relevant and useful to practitioners in the field.<sup>22<\/sup> This effort, along with the IACP Law Enforcement Policy Center\u2019s forthcoming guidance for implementing technology, will provide law enforcement agencies with broad guidance on specific issues related to technology implementation once they are published.<sup>23<\/sup> There are also resources regarding sample facial recognition policies, such as the U.S. Bureau of Justice Assistance <em>Face Recognition Policy Development Template<\/em> and many other existing policies available from police departments throughout the United States.<sup>24<\/sup><\/p>\n<h2><strong><mark>Summary<\/mark><\/strong><\/h2>\n<p>Facial recognition can literally help law enforcement save lives\u2014there a multitude of examples of its power to do just that. But there are just as many examples of critics\u2019 questions being unanswered or of distorted media views being unchecked by police leaders.<\/p>\n<p>With simple, transparent explanations regarding law enforcement facial recognition, many of those opposed to it will have a better understanding of its use and perhaps see it the way public safety professionals view it\u2014a responsibly deployed tool that police can use to promote public safety.<\/p>\n<aside class=\"pullout alignleft\"><\/p>\n<p><strong><big>IACP Resources<\/big><\/strong><\/p>\n<ul style=\"list-style-type: square;\">\n<li><a href=\"https:\/\/www.theiacp.org\/resources\/document\/law-enforcement-facial-recognition-use-case-catalog\" target=\"_blank\" rel=\"noopener noreferrer\">Law Enforcement Facial Recognition Use Case Catalog<\/a><\/li>\n<li><em><a href=\"https:\/\/www.theiacp.org\/resources\/document\/guiding-principles-for-law-enforcements-use-of-facial-recognition-technology\" target=\"_blank\" rel=\"noopener noreferrer\">Guiding Principles for Law Enforcement\u2019s Use of Facial Recognition Technology<\/a><\/em><\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.theiacp.org\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>theIACP.org<\/strong><\/a><\/p>\n<ul style=\"list-style-type: square;\">\n<li>\u201c<a href=\"https:\/\/www.policechiefmagazine.org\/a-practical-application-facial-recognition-technology\/\">A Practical Application: Facial Recognition Technology<\/a>\u201d (article)<\/li>\n<\/ul>\n<p><\/aside>\n<p>Law enforcement agencies can also benefit by \u201cgetting ahead\u201d of criticism as opposed to just responding to it. The best way to accomplish this is by having well-vetted policies on hand to fall back on should questions arise regarding deployment or use. Once agencies establish solid policy rationale for facial recognition with proper documentation, training, and community transparency, it will be time to go on the \u201coffense\u201d and start publicizing the many success stories this powerful tool generates.<\/p>\n<p>Many changes are likely to come regarding facial recognition, including determination of how pandemic protections like face masks will affect its accuracy. Information will continue to flow from detractors, watchdog groups, vendors, and even scientists. It is critical that police leaders also make their voices heard on how they are using facial recognition; the many protections implemented via policies regarding this powerful tool; and of course, how it impacts their ability to ensure public safety in the modern world.\u00a0<span style=\"font-family: Webdings;\">🛡<\/span><\/p>\n<p><strong>Notes:<\/strong><\/p>\n<p><sup>1<\/sup> Clare Garvie, Alvaro Bedoya, and Jonathan Frankle, <a href=\"https:\/\/www.perpetuallineup.org\/\"><em>The Perpetual Line-up: Unregulated Police Face Recognition in America<\/em><\/a> (Washington, DC: Georgetown Law, Center on Privacy & Technology, 2016).<\/p>\n<p><sup>2<\/sup> Garvie, Bedoya, and Frankle, <a href=\"https:\/\/www.perpetuallineup.org\/\"><em>The Perpetual Line-up<\/em><\/a><em>.<\/em><\/p>\n<p><sup>3<\/sup> Garvie, Bedoya, and Frankle, <a href=\"https:\/\/www.perpetuallineup.org\/\"><em>The Perpetual Line-up<\/em><\/a><em>.<\/em><\/p>\n<p><sup>4<\/sup> Daniel Castro, \u201c<a href=\"https:\/\/www.wsj.com\/articles\/should-government-halt-the-use-of-facial-recognition-technology-11582513260\">Should Government Halt the Use of Facial-Recognition Technology<\/a><em>?\u201d<\/em> <em>Wall Street Journal<\/em>, February 24, 2020.<\/p>\n<p><sup>5<\/sup> Ryan Tracey, \u201c<a href=\"https:\/\/www.wsj.com\/articles\/tech-firms-seek-to-head-off-bans-on-facial-recognition-11583498034\">Tech Firms Seek to Head Off Bans on Facial Recognition<\/a><em>,\u201d<\/em> <em>Wall Street Journal<\/em>, March 9, 2020.<\/p>\n<p><sup>6<\/sup> Ryan Tracey, \u201c<a href=\"https:\/\/www.wsj.com\/articles\/aclu-sues-u-s-for-records-of-facial-recognition-use-at-airports-11584025151\">ACLU Sues U.S. for Records of Facial-Recognition Use at Airports<\/a>,\u201d <em>Wall Street Journal<\/em>, March 12, 2020.<\/p>\n<p><sup>7<\/sup> Joseph O\u2019Sullivan, \u201c<a href=\"https:\/\/www.seattletimes.com\/seattle-news\/politics\/washington-senate-passes-bill-to-regulate-governments-use-of-facial-recognition-technology\/\">Washington Senate Passes Bill to Regulate Governments\u2019 Use of Facial-Recognition Technology<\/a><em>,<\/em>\u201d <em>Seattle Times<\/em>, Feb. 19, 2020.<\/p>\n<p><sup>8<\/sup> Jessica Lusamba, \u201c<a href=\"https:\/\/www.jurist.org\/news\/2019\/11\/bill-to-restrict-police-use-of-facial-recognition-introduced-in-us-senate\/\">Bill to Restrict Police Use of Facial Recognition Introduced in U.S. Senate<\/a>,\u201d <em>Jurist<\/em>, November 15, 2019.<\/p>\n<p><sup>9<\/sup> Corinne Reichert, \u201c<a href=\"https:\/\/www.cnet.com\/news\/facial-recognition-could-be-temporarily-banned-for-law-enforcement-use\/\">Facial Recognition Could Be Temporarily Banned for Law Enforcement Use<\/a>,\u201d Cnet, February 12, 2020.<\/p>\n<p><sup>10<\/sup> <a href=\"https:\/\/www.merkley.senate.gov\/imo\/media\/doc\/20.02.12%20Facial%20Recognition.pdf\">Ethical Use of Facial Recognition Act<\/a>, S. 3284, 116th Cong. (2020).<\/p>\n<p><sup>11<\/sup> Umberto Bacchi for the Thomson Reuters Foundation, \u201c<a href=\"https:\/\/www.reuters.com\/article\/us-britain-tech-privacy\/landmark-uk-court-ruling-finds-police-use-of-facial-recognition-unlawful-idUSKCN2572B8\">Landmark UK Court Ruling Finds Police Use of Facial Recognition Unlawful<\/a>,\u201d Reuters, August 11, 2020.<\/p>\n<p><sup>12<\/sup> Christopher Carbone, \u201c<a href=\"https:\/\/www.foxnews.com\/tech\/boston-bans-police-facial-recognition-technology\">Boston Bans Police from Using Facial Recognition Technology<\/a>,\u201d Fox News, June 24, 2020; Kyle Wiggers, \u201c<a href=\"https:\/\/venturebeat.com\/2020\/07\/22\/new-york-bans-use-of-facial-recognition-in-schools-statewide\/\">New York Bans Use of Facial Recognition in Schools Statewide<\/a>,\u201d <em>VentureBeat<\/em>, July 22, 2020; Randy Billings, \u201c<a href=\"https:\/\/www.pressherald.com\/2020\/08\/03\/portland-councilors-approve-ban-on-facial-recognition-technology\/\">Portland Councilors Approve Ban on Facial Recognition Technology<\/a>,\u201d <em>Portland Press Herald<\/em>, updated August 4, 2020.<\/p>\n<p><sup>13<\/sup> International Biometrics + Identity Association (IBIA), <a href=\"https:\/\/www.ibia.org\/download\/datasets\/5124\/NIST%20Report%20on%20Facial%20Recognition-%20A%20Game%20Changer.pdf\"><em>NIST Report on Facial Recognition: A Game Changer<\/em><\/a> (Washington, DC: IBIA, 2020); Michael McLaughlin and Daniel Castro, \u201c<a href=\"https:\/\/itif.org\/publications\/2020\/01\/27\/critics-were-wrong-nist-data-shows-best-facial-recognition-algorithms\">The Critics Were Wrong: NIST Data Shows the Best Facial Recognition Algorithms Are Neither Racist Nor Sexist<\/a>,\u201d ITIF, January 27, 2020.<\/p>\n<p><sup>14<\/sup> Patrick Grother, Mei Ngan, and Kayee Hanaoka, <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf\"><em>Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects<\/em><\/a> (Washington, DC: National Institute of Standards and Technology, 2019).<\/p>\n<p><sup>15<\/sup> Grother, Ngan, and Hanaoka, <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf\"><em>Face Recognition Vendor Test (FRVT) Part <\/em>3<\/a>, 1.<\/p>\n<p><sup>16<\/sup> Grother, Ngan, and Hanaoka, <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf\"><em>Face Recognition Vendor Test (FRVT) Part <\/em>3<\/a>, 1.<\/p>\n<p><sup>17<\/sup> Grother, Ngan, and Hanaoka, <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf\"><em>Face Recognition Vendor Test (FRVT) Part <\/em>3<\/a>, 2.<\/p>\n<p><sup>18<\/sup> Grother, Ngan, and Hanaoka, <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf\"><em>Face Recognition Vendor Test (FRVT) Part <\/em>3<\/a>, 3.<\/p>\n<p><sup>19<\/sup> Grother, Ngan, and Hanaoka, <a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf\"><em>Face Recognition Vendor Test (FRVT) Part <\/em>3<\/a>, 3.<\/p>\n<p><sup>20<\/sup> IJIS Institute and IACP Law Enforcement Imaging Technology Task Force, <a href=\"https:\/\/www.theiacp.org\/resources\/document\/law-enforcement-facial-recognition-use-case-catalog\"><em>Law Enforcement<\/em> <em>Facial Recognition Use Case Catalog<\/em><\/a> (IJIS Institute and IACP, 2019).<\/p>\n<p><sup>21<\/sup> IACP Criminal Justice Information Systems (CJIS) Committee, <a href=\"https:\/\/www.theiacp.org\/sites\/default\/files\/2019-10\/LE%20Facial%20Rec%20Guiding%20Principles%20Document%20July%202019.pdf\"><em>Guiding Principles for Law Enforcement Use of Facial Recognition Technology<\/em><\/a><em>,<\/em>\u201d IACP, 2019.<\/p>\n<p><sup>22<\/sup> IACP CJIS Committee, \u201cModel Face Recognition Policy,\u201d 2019.<\/p>\n<p><sup>23<\/sup> IACP Law Enforcement Policy Center, <em>Considerations Document and Concepts & Issues Paper on Implementing Technology, 2020<\/em>.<\/p>\n<p><sup>24<\/sup> U.S. Bureau of Justice Assistance (BJA), <a href=\"https:\/\/www.google.com\/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjR3rLEr97pAhVailwKHdZ6Bu8QFjAAegQIBRAB&url=https%3A%2F%2Fit.ojp.gov%2FGIST%2F1204%2FFile%2FFINAL-Face%2520Recognition%2520Policy%2520Development%2520Template.pdf%2F&usg=AOvVaw1ynEg7kLz0EVa7z3YXaNEh\"><em>Face Recognition Policy Development Template for<\/em> <em>State, Local, and Tribal Criminal Intelligence and Investigative Activities<\/em><\/a> (Washington, DC: BJA, 2017).<\/p>\n<p> <\/p>\n<table style=\"width: 100%; border-collapse: collapse; border-color: #102c4e; background-color: #102c4e;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 100%; vertical-align: middle;\"> <\/p>\n<p class=\"Contributors_Contributor-Description\" style=\"padding-left: 40px;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-59027 size-full\" src=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski.jpg\" alt=\"\" width=\"169\" height=\"169\" srcset=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski.jpg 169w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Zaworski-150x150.jpg 150w\" sizes=\"auto, (max-width: 169px) 100vw, 169px\" \/><span style=\"color: #ffffff;\"><big><strong>Martin J. Zaworksi<\/strong>, PhD, PMP, currently teaches in the Graduate Homeland Security program at Florida International University. He served 30+ years in law enforcement and is a retired captain (Baltimore County, Maryland, Police Department); former division commander (Miami Beach, Florida, Police Department); and former bureau chief (Broward County, Florida, Sheriff\u2019s Office).<\/big><\/span><\/p>\n<p> <\/td>\n<\/tr>\n<tr>\n<td style=\"width: 100%; vertical-align: middle;\">\n<p class=\"Contributors_Contributor-Description\" style=\"padding-left: 40px;\"><span style=\"color: #ffffff;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-59038\" src=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle-231x300.jpg\" alt=\"\" width=\"169\" height=\"219\" srcset=\"https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle-231x300.jpg 231w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle-789x1024.jpg 789w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle-768x997.jpg 768w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle-1183x1536.jpg 1183w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle-1577x2048.jpg 1577w, https:\/\/www.policechiefmagazine.org\/wp-content\/uploads\/Doyle.jpg 1581w\" sizes=\"auto, (max-width: 169px) 100vw, 169px\" \/><big><strong>Patrick Doyle<\/strong> is the chairman of the Joint IACP\/IJIS Institute Law Enforcement Imaging Technology Task Force. A retired New Jersey state trooper, he also served as the senior watch officer at the largest U.S. fusion center and in multiple command-level liaison positions with the FBI, U.S. Secret Service, and New York City Police Department.<\/big><\/span><\/p>\n<p> <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Please cite as<\/p>\n<p style=\"padding-left: 40px;\">Martin Zaworski and Patrick Doyle, \u201cGetting in Front of Facial Recognition: The Criticism, Science, and Response,\u201d <em>Police Chief\u00a0<\/em>87, no.10 (2020): 52\u201359.<\/p>\n<p> <\/p>\n","protected":false},"excerpt":{"rendered":"<p>It should come as no surprise that facial recognition software is seen by many law enforcement officials as a valuable tool to help identify or locate people, either to assist victims or bring offenders to justice. Anecdotal evidence suggests that this technology, when used in concert with other available investigative resources and processes, can significantly help resolve criminal cases and increase public safety.<\/p>\n","protected":false},"author":4042,"featured_media":59014,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[142,146],"tags":[],"class_list":["post-59009","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-policy","category-technology"],"acf":{"subtitle":"The Criticism, Science, and Response","post_author":"Martin Zaworski, PhD, Adjunct Professor, Florida International University, and Patrick Doyle, Lieutenant (Ret.), New Jersey State Police","main_category":"Technology","legacy_article_id":"","legacy_issue_id":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.4 (Yoast SEO v24.4) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Getting in Front of Facial Recognition - Police Chief Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.policechiefmagazine.org\/getting-in-front-of-facial-recognition\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Getting in Front of Facial Recognition\" \/>\n<meta property=\"og:description\" content=\"It should come as no surprise that facial recognition software is seen by many law enforcement officials as a valuable tool to help identify or locate people, either to assist victims or bring offenders to justice. 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