Snap Shot
- The question: How accurately do people recognize and match faces, and what does that mean for eyewitness identifications, identifications from surveillance images, expert facial comparison, and facial recognition technology?
- What the research shows: Familiar faces are recognized with high accuracy across wide variation in image quality, although familiarity is a matter of degree and accuracy falls when the face is covered or disguised. Recognizing or matching unfamiliar faces is much harder, and errors are common even with two good photographs side by side. Trained examiners and super-recognizers do better on average than untrained viewers, and the best recent algorithms performed within the examiners’ range in published tests.
- What it does not show: Group error rates cannot show whether a particular identification or comparison is right or wrong. Little research addresses moderately familiar viewers, and benchmark accuracy for an algorithm does not establish the accuracy of a particular search.
- The law: A lay witness who knows a defendant may identify him in a surveillance image if the witness has an advantage over the jury (Fed. R. Evid. 701; Mil. R. Evid. 701; Ariz. R. Evid. 701; State v. King). Expert facial comparison is tested under Rule 702, and New Jersey has defined discovery about facial recognition searches (State v. Miles).
- Issues for each side: Prosecutors can rely on identifications by people who genuinely know the defendant but should not present resemblance, or a facial recognition match, as proof of identity. Defense counsel can show how hard unfamiliar face matching is but should recognize that familiar-face recognition is robust and that trained examiners outperform untrained viewers.
Almost every contested identification depends on face recognition: an eyewitness recognizing a stranger seen briefly, an investigator or juror comparing a surveillance still with a booking photograph, a relative identifying a person in a video, or an examiner or algorithm comparing two images. These tasks look alike but differ greatly in accuracy, and the most important difference is whether the viewer already knows the person.
Face recognition errors are usually sincere: a person who confuses two similar-looking strangers is not lying. This paper reviews how faces are processed, the gap between familiar and unfamiliar faces, face matching, expertise, face coverings, and facial recognition technology, and then the federal, military, and Arizona law on identification from images. Separate papers cover the cross-race effect, composites, and children’s face recognition.
How Faces Are Processed
Faces engage a kind of processing that most objects do not. Inverting a face impairs recognition more than inverting other familiar objects (Yin, 1969). Aligned top and bottom halves of two different faces fuse into what looks like a new face and become hard to identify, a difficulty that disappears when the halves are offset (Young et al., 1987), and a feature such as a nose is recognized better within the face in which it was learned than in isolation (Tanaka & Farah, 1993). This holistic processing, perceiving faces as integrated wholes, is reliable, although its measured size depends on the method (Richler & Gauthier, 2014).
A feature-by-feature description therefore captures only part of what people use, one reason witnesses can recognize faces they cannot describe. An influential model also separates perceiving a face’s structure from the later stages at which it is linked to knowledge of a person and a name (Bruce & Young, 1986).
Familiar and Unfamiliar Faces
For legal purposes, the central finding is that known and unknown faces are recognized with very different accuracy (Hancock et al., 2000). People recognize friends, relatives, and coworkers easily across changes in lighting, angle, expression, age, and image quality, and are much less accurate with faces seen once or in only a few images. Recognition of unfamiliar faces relies heavily on external features such as hairstyle and face shape, which are easy to change, while familiarity shifts reliance toward the eyes, nose, and mouth (Lane & Houston, 2021, pp. 44–45).
One explanation is that familiarity is built from variability. People come to know a face by seeing it in many forms, and a representation averaged across many images supports more robust recognition than any single image (Burton et al., 2005). In a card-sorting study, participants who did not know two people sorted 40 photographs of them into many more identities than were present, while participants who knew them sorted almost perfectly (Jenkins et al., 2011). Photographs of one unfamiliar person can look like different people, and photographs of two similar-looking people can look like one.
Surveillance research shows the contrast. Viewers who knew the people in poor-quality security video identified them with high accuracy, while viewers who did not, including experienced police officers, performed poorly; obscuring the face sharply reduced familiar viewers’ accuracy, so they relied on the face rather than body or gait (Burton et al., 1999). In matching video images to photographs, personally familiar viewers were highly accurate, unfamiliar viewers were not, and brief familiarization helped little (Bruce et al., 2001).
Two qualifications matter in court. Familiarity is a matter of degree, and research has not established how accurate people are with moderately familiar faces, such as an officer who has come to “know” a suspect through photographs or earlier contacts (Lane & Houston, 2021, pp. 54–57). And many cases do not involve strangers: Lane and Houston (2021, pp. 44, 56) cite a National Institute of Justice estimate that most sexual assaults reported by women involve a perpetrator the victim knew, where identity is rarely disputed and recognition of a well-known person is among the most reliable forms of memory.
Matching Unfamiliar Faces
Matching, deciding whether two images in view show the same person, removes memory from the task, yet errors remain common. Ability varies widely, and performance with upright unfamiliar faces correlates strongly with performance on inverted faces, suggesting that unfamiliar faces are processed in a qualitatively different way from familiar ones (Megreya & Burton, 2006). Bruce et al. (1999) found poor accuracy when participants matched a face on video to an array of photographs, and accuracy fell further when viewpoint and expression differed between the video and the array; letting the video be replayed as often as needed improved performance only slightly. Lane and Houston (2021, pp. 46–47) summarize the best-case result as about 70% correct; this paper did not read the primary article for that figure. The Glasgow Face Matching Test, built from photograph pairs taken with different cameras, provides normative data for this ability (Burton et al., 2010).
Professional experience does not by itself solve the problem. In a field study of photo credit cards, cashiers often accepted a card showing a different but similar-looking person (Kemp et al., 1997). Passport officers comparing photographs to live card-holders falsely accepted 14% of fraudulent photographs and did no better than students on a standardized test; accuracy was unrelated to years of experience (White et al., 2014). Reviewers of candidate lists from facial recognition software face the same difficulty: participants erred on more than half of adult targets, passport officers who used the system daily did no better than students, and trained facial examiners outperformed both by about 20 percentage points (White et al., 2015). Resemblance between two images, even good ones, does not by itself establish identity.
Individual Differences, Super-Recognizers, and Trained Examiners
Face recognition ability ranges from developmental prosopagnosia, in which people have difficulty recognizing even familiar people, to super-recognizers, whose ability falls well above the normal range (Russell et al., 2009). Recognition is also generally better for faces of one’s own group than for faces of other groups (Meissner & Brigham, 2001), a finding discussed in The Cross-Race Effect.
The most direct comparison of experts and algorithms tested forensic facial examiners, facial reviewers, super-recognizers, fingerprint examiners, and students on 20 very difficult image pairs (Phillips et al., 2018). Examiners, reviewers, and super-recognizers were more accurate on average than the other groups, but accuracy varied widely within every group. Four deep neural network algorithms developed between 2015 and 2017 performed within the human range, the most recent above the examiners’ median. Combining examiners’ judgments improved accuracy, and one examiner combined with the best algorithm outperformed two examiners combined. The algorithms tested are now dated.
Similar accuracy can conceal different routes to it. Examiners used the full rating scale and agreed with each other more, while super-recognizers’ judgments clustered at high confidence (Hahn et al., 2022). In a 2023 study that included an international proficiency test, super-recognizers were fast, biased toward “same person,” and sometimes confidently wrong, while examiners were slower, unbiased, and avoided misidentifications; combining types of expertise improved accuracy (Towler et al., 2023). A witness’s title, training, or reported talent indicates average group performance, not the accuracy of a particular comparison.
Disguise and Face Coverings
Deliberate disguise takes two forms: evasion (trying not to look like oneself) and impersonation (trying to look like someone else). Both impaired unfamiliar viewers; familiar viewers saw through impersonation, but evasion reduced their accuracy too (Noyes & Jenkins, 2019).
Surgical masks added to photographs produced a large drop in matching accuracy for familiar and unfamiliar faces alike (Carragher & Hancock, 2020). Masks and sunglasses both reduced accuracy; super-recognizers were also impaired but as a group remained more accurate than controls (Noyes et al., 2021). Real face coverings impaired typical viewers, super-recognizers, and algorithms more than digitally added masks, suggesting that earlier studies underestimated the effect (Ritchie et al., 2024). Caps, hoods, sunglasses, and masks are common in surveillance footage, and identification from a partly covered face, by a person or a machine, warrants corresponding caution.
Facial Recognition Technology in Policing
Facial recognition technology compares a probe image with database images and returns candidates ranked by similarity. The National Institute of Standards and Technology tested 189 algorithms from 99 developers and found that most showed demographic differentials (Grother et al., 2019). In one-to-one verification, false positive rates for Asian and African American faces were often 10 to 100 times higher than for Caucasian faces, depending on the algorithm; in one-to-many searches of a mugshot database, false positives were higher for African American women. Differentials varied widely across algorithms, and the most equitable were also among the most accurate. These results date from 2019.
A 2024 National Academies report found that demographic differentials have not been entirely eliminated, even in the most accurate algorithms, and that false negatives can become significant with low-quality images (National Academies of Sciences, Engineering, and Medicine, 2024). It noted at least six high-profile wrongful arrests linked to the technology, all of Black individuals, while observing that no comprehensive data show how often the technology is used or how often it contributes to arrests, accurate or not. It recommended that a result be one component in developing investigative leads and only part of a multi-factor basis for an arrest, that a human remain in the loop, and that operators be trained.
Human reviewers’ accuracy can limit the system’s (White et al., 2015). A candidate is returned because he or she resembles the probe image, so an eyewitness shown that person may identify him or her confidently even if innocent. Researchers from both the “reliability” and “vulnerability” traditions agreed that suspects should be selected on evidence independent of facial resemblance, citing facial recognition searches as an example (Lindsay et al., 2025). Equally, a lead from the technology can be accurate and can be confirmed by independent evidence, and its role in an investigation does not by itself show that an identification is wrong.
Limits of the Evidence
Most research uses controlled photographs, student participants, and low stakes; deliberately difficult image sets may understate accuracy for easier real comparisons. Familiarity is usually treated as all-or-nothing, studies of professional groups often have small samples, and algorithm results date quickly; benchmark accuracy does not establish the accuracy of a particular search. Research reports group averages and wide individual differences, and it cannot determine whether a particular witness, examiner, or algorithm was correct in a particular case.
Questions to Ask About This Research
When Phillips et al. (2018) is cited to support an examiner’s conclusion, counsel should ask whether this examiner’s proficiency has been tested, because accuracy varied widely within every group, and how the case images compare with the study’s difficult pairs. When White et al. (2014) or Kemp et al. (1997) is cited to challenge an identification from images, counsel should ask how well the viewer knew the person, because those studies concern unfamiliar matching. When Burton et al. (1999) is cited to support identification by an acquaintance, counsel should ask how long and how recently the witness knew the defendant, whether the face was covered (Noyes & Jenkins, 2019; Ritchie et al., 2024), and whether the witness was told whom to expect. When the NIST findings (Grother et al., 2019) are cited, counsel should ask which algorithm, version, and threshold were used, because differentials varied widely across algorithms, and whether a human reviewer, not only the software, selected the candidate. These questions are explained in “Reading Research Claims: A Guide for Attorneys” on this site.
Face Recognition and the Law
Federal and Military Standards
Federal Rule of Evidence 701 permits a lay opinion rationally based on the witness’s perception, helpful to the jury, and not based on specialized knowledge; Military Rule of Evidence 701 is to the same effect. Federal courts allow a lay witness who knows a defendant to identify him in a surveillance image when there is some basis for concluding that the witness is more likely than the jury to do so correctly. In United States v. Jackman (1995), the First Circuit upheld identifications from blurred bank surveillance photographs by the defendant’s former wife and two longtime acquaintances, because the photographs were neither so clear nor so obscure that the witnesses had no advantage over the jury. United States v. Pierce (1998) treated familiarity as the critical factor and urged caution under Rule 403 when the witness is a police or probation officer, and United States v. LaPierre (1993) found familiarity acquired only from photographs and others’ descriptions insufficient. This review did not locate a published military appellate decision applying Mil. R. Evid. 701 to identification from surveillance images.
Expert facial comparison is governed by Rule 702, as amended December 1, 2023, under which the proponent must show it is more likely than not that the testimony rests on sufficient facts or data and reliable methods reliably applied. Daubert v. Merrell Dow Pharmaceuticals, Inc. (1993) made the trial judge a gatekeeper, and Kumho Tire Co. v. Carmichael (1999) extended the gatekeeping obligation to all expert testimony, including technical or other specialized knowledge. In United States v. Dorsey (1995), the Fourth Circuit upheld exclusion of defense forensic anthropologists’ comparisons of surveillance photographs with photographs of the defendant, because the method had not been shown to be tested, peer reviewed, or of known error rate, and jurors could compare the photographs themselves. Later research provides group data on examiner accuracy (Phillips et al., 2018), but admissibility turns on the method and its application in the case. In courts-martial, admissibility is assessed under United States v. Houser (1993), and the 2023 amendment became part of Mil. R. Evid. 702 through Mil. R. Evid. 1102 in 2025 (Joint Service Committee on Military Justice, 2025); practitioners should confirm the current text. Due process challenges to suggestive identification procedures, discussed in Eyewitness Memory, are limited by Perry v. New Hampshire (2012) to suggestion arranged by law enforcement.
Facial recognition technology has prompted discovery litigation elsewhere. In State v. Miles (2026), the New Jersey Supreme Court held that the defendant was entitled to basic information identifying the software and its publicly available performance documentation, and to discovery about how the State used it, including the probe image and returned images, treating that use as relevant to the defense although the State described the technology as an investigative tool. It reversed without prejudice an order compelling proprietary material such as source code, which may be sought on a showing of particularized need, and declined to read State v. Arteaga (2023) as a fixed checklist. The decision binds only New Jersey courts.
Arizona
Arizona Rule of Evidence 701 follows the federal formulation. In State v. King (1994), the Arizona Supreme Court upheld admission of testimony by acquaintances who had known the defendant at the time of the crime identifying him in surveillance videotape and photographs, because his appearance had changed before trial; the court distinguished testimony on whether the defendant committed the charged crime, which it described, in discussing Fuenning v. Superior Court (1983), a DUI breath-test foundation case, as not advisable. Expert facial comparison is subject to Arizona Rule of Evidence 702, which adopted the Daubert standard effective January 1, 2012 and was amended effective January 1, 2024 to track the 2023 federal amendment (Ariz. Sup. Ct. Order R-23-0004). Pretrial identification procedures are challenged at a hearing under State v. Dessureault (1969). State v. Chapple (1983), Arizona’s leading case on eyewitness expert testimony, held exclusion of that testimony an abuse of discretion on its facts; admission otherwise remains discretionary (State v. Nordstrom, 2001). This review did not locate an Arizona appellate decision or statute specifically governing police use of facial recognition technology.
Special Issues in Legal Practice
The principles described above apply with equal force to every party in a legal proceeding. The science operates the same way regardless of who is asking the questions, and the research summarized here does not by itself favor either side. What differs between the parties is not the science but the vantage point — the particular places where these issues tend to surface, and the particular errors each party is best positioned to guard against. The two summaries below identify those recurring issues. They are offered for orientation, not as litigation strategy; in every case the controlling question is what the evidence in that case will, and will not, support, and the expert’s role is to inform the trier of fact rather than to advocate for either side.
Special Issues for Prosecutors
The research supports several propositions prosecutors can legitimately present. Recognition of familiar people is robust, even from poor surveillance images, and a witness who knew the defendant well at the time of the offense may have an advantage over jurors in saying who appears in an image. Trained facial examiners outperform untrained viewers on average, and a facial recognition lead confirmed by independent evidence has served its proper role. In many sexual assault cases the complainant knew the accused, and research on stranger identification does not apply to recognition of a well-known person. Prosecutors can strengthen identification evidence by documenting the basis and extent of a witness’s familiarity, recording how and when a witness first viewed an image, and developing evidence of identity independent of facial resemblance.
The research also counsels caution. An impression that two images of an unfamiliar person look alike is error-prone evidence of identity: in studies of matching a video still to a photo array, accuracy was poor and fell when viewpoint and expression differed (Bruce et al., 1999), and a secondary summary reports about 70% correct for a high-quality still with unlimited time (Lane & Houston, 2021, pp. 46–47; the primary figure was not checked). A facial recognition candidate is selected for resemblance and may be confidently identified by an eyewitness even if innocent, and algorithm accuracy varies with the algorithm, the image, and the demographic group. Treating an examiner’s title, a super-recognizer’s reputation, or a similarity score as proof of identity goes beyond the research, and disguise can reduce accuracy even for familiar viewers.
Special Issues for Defense
The research supports several propositions defense counsel can legitimately present. Matching or recognizing unfamiliar faces is error-prone, even for professionals, and an eyewitness identification of a stranger rests on that weaker ability. Familiarity acquired only from photographs or brief contacts has not been shown to confer the accuracy of knowing a person well. Face coverings and disguise reduce accuracy, algorithms have shown demographic differentials, and resemblance-based suspect selection carries a specific risk of confident misidentification. Where discovery rules permit, information about how a facial recognition search was conducted can help the factfinder evaluate the identification.
The research also counsels caution about the limits that apply to the defense. Familiar-face recognition is among the most reliable forms of memory, and an acquaintance’s identification is not necessarily weakened because the image is poor. Trained examiners are, as a group, more accurate than untrained viewers, and the best algorithms tested performed within the human range on very difficult image pairs (Phillips et al., 2018), and the most equitable algorithms were also among the most accurate (Grother et al., 2019), though benchmark accuracy does not establish the accuracy of a particular search, so a categorical attack on facial comparison overstates the research. An expert may explain the conditions that affect accuracy but cannot tell the factfinder that a particular identification or comparison is wrong.
This paper is for educational purposes only and is not legal advice. The author is a psychologist, not an attorney; summaries of law are general and must be verified against current authority or with a licensed attorney.
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Legal Authorities
Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).
Fuenning v. Superior Court, 139 Ariz. 590, 680 P.2d 121 (1983).
Kumho Tire Co. v. Carmichael, 526 U.S. 137 (1999).
Perry v. New Hampshire, 565 U.S. 228 (2012).
State v. Arteaga, 476 N.J. Super. 36, 296 A.3d 542 (App. Div. 2023).
State v. Chapple, 135 Ariz. 281, 660 P.2d 1208 (1983).
State v. Dessureault, 104 Ariz. 380, 453 P.2d 951 (1969).
State v. King, 180 Ariz. 268, 883 P.2d 1024 (1994).
State v. Miles, 264 N.J. 209, 357 A.3d 903 (2026).
State v. Nordstrom, 200 Ariz. 229, 25 P.3d 717 (2001).
United States v. Dorsey, 45 F.3d 809 (4th Cir. 1995).
United States v. Houser, 36 M.J. 392 (C.M.A. 1993).
United States v. Jackman, 48 F.3d 1 (1st Cir. 1995).
United States v. LaPierre, 998 F.2d 1460 (9th Cir. 1993).
United States v. Pierce, 136 F.3d 770 (11th Cir. 1998).
Fed. R. Evid. 701.
Fed. R. Evid. 702.
Ariz. R. Evid. 701.
Ariz. R. Evid. 702.
Mil. R. Evid. 701.
Mil. R. Evid. 702.
Mil. R. Evid. 1102.
Related topic papers: Eyewitness Memory · The Cross-Race Effect · Memory for Persons and Faces · Face Composites · Children’s Face Recognition · Lineup Construction