SignificanceThis study measures face identification accuracy for an international group of professional forensic facial examiners working under circumstances that apply in real world casework. Examiners and other human face “specialists,” including forensically trained facial reviewers and untrained superrecognizers, were more accurate than the control groups on a challenging test of face identification. Therefore, specialists are the best available human solution to the problem of face identification. We present data comparing state-of-the-art face recognition technology with the best human face identifiers. The best machine performed in the range of the best humans: professional facial examiners. However, optimal face identification was achieved only when humans and machines worked in collaboration.

Face masks present a new challenge to face identification (here matching) and emotion recognition in Western cultures. Here, we present the results of three experiments that test the effect of masks, and also the effect of sunglasses (an occlusion that individuals tend to have more experienced with) on (i) familiar face matching, (ii) unfamiliar face matching and (iii) emotion categorization. Occlusion reduced accuracy in all three tasks, with most errors in the mask condition; however, there was little difference in performance for faces in masks compared with faces in sunglasses. Super-recognizers, people who are highly skilled at matching unconcealed faces, were impaired by occlusion, but at the group level, performed with higher accuracy than controls on all tasks. Results inform psychology theory with implications for everyday interactions, security and policing in a mask-wearing society.

Face recognition is used to prove identity across a wide variety of settings. Despite this, research consistently shows that people are typically rather poor at matching faces to photos. Some professional groups, such as police and passport officers, have been shown to perform just as poorly as the general public on standard tests of face recognition. However, face recognition skills are subject to wide individual variation, with some people showing exceptional ability—a group that has come to be known as ‘super-recognisers’. The Metropolitan Police Force (London) recruits ‘super-recognisers’ from within its ranks, for deployment on various identification tasks. Here we test four working super-recognisers from within this police force, and ask whether they are really able to perform at levels above control groups. We consistently find that the police ‘super-recognisers’ perform at well above normal levels on tests of unfamiliar and familiar face matching, with degraded as well as high quality images. Recruiting employees with high levels of skill in these areas, and allocating them to relevant tasks, is an efficient way to overcome some of the known difficulties associated with unfamiliar face recognition.

Face identification is reliable for viewers who are familiar with the face, and unreliable for viewers who are not. One account of this contrast is that people become good at recognising a face by learning its configuration-the specific pattern of feature-to-feature measurements. In practice, these measurements differ across photos of the same face because objects appear more flat or convex depending on their distance from the camera. Here we connect this optical understanding to face configuration and identification accuracy. Changing camera-to-subject distance (0.32m versus 2.70m) impaired perceptual matching of unfamiliar faces, even though the images were presented at the same size. Familiar face matching was accurate across conditions. Reinstating valid distance cues mitigated the performance cost, suggesting that perceptual constancy compensates for distance-related changes in optical face shape. Acknowledging these distance effects could reduce identification errors in applied settings such as passport control.

There are large individual differences in people’s face recognition ability. These individual differences provide an opportunity to recruit the best face-recognisers into jobs that require accurate person identification, through the implementation of ability-screening tasks. To date, screening has focused exclusively on face recognition ability; however real-world identifications can involve the use of other person-recognition cues. Here we incorporate body and biological motion recognition as relevant skills for person identification. We test whether performance on a standardised face-matching task (the Glasgow Face Matching Test) predicts performance on three other identity-matching tasks, based on faces, bodies, and biological motion. We examine the results from group versus individual analyses. We found stark differences between the conclusions one would make from group analyses versus analyses that retain information about individual differences. Specifically, tests of correlation and analysis of variance suggested that face recognition ability was related to performance for all person identification tasks. These analyses were strikingly inconsistent with the individual differences data, which suggested that the screening task was related only to performance on the face task. This study highlights the importance of individual data in the interpretation of results of person identification ability.