Face identification proficiency test designed using item response theory.

Measures of face-identification proficiency are essential to ensure accurate and consistent performance by professional forensic face examiners and others who perform face-identification tasks in applied scenarios. Current proficiency tests rely on static sets of stimulus items and so cannot be administered validly to the same individual multiple times. To create a proficiency test, a large number of items of "known" difficulty must be assembled. Multiple tests of equal difficulty can be constructed then using subsets of items. We introduce the Triad Identity Matching (TIM) test and evaluate it using item response theory (IRT). Participants view face-image "triads" (N = 225) (two images of one identity, one image of a different identity) and select the different identity. In Experiment 3, university students (N = 197) showed wide-ranging accuracy on the TIM test, and IRT modeling demonstrated that the TIM items span various difficulty levels. In Experiment 3, we used IRT-based item metrics to partition the test into subsets of specific difficulties. Simulations showed that subsets of the TIM items yielded reliable estimates of subject ability. In Experiments 3a and b, we found that the student-derived IRT model reliably evaluated the ability of non-student participants and that ability generalized across different test sessions. In Experiment 3c, we show that TIM test performance correlates with other common face-recognition tests. In summary, the TIM test provides a starting point for developing a framework that is flexible and calibrated to measure proficiency across various ability levels (e.g., professionals or populations with face-processing deficits).

Forensic facial examiners versus super-recognizers: Evaluating behavior beyond accuracy.

We evaluated the detailed, behavioral properties of face matching performance in two specialist groups: forensic facial examiners and super-recognizers. Both groups compare faces to determine identity with high accuracy and outperform the general population. Typically, facial examiners are highly trained; super-recognizers rely on natural ability. We found distinct behaviors between these two groups. Examiners used the full 7-point identity judgment scale (-3: "different"; +3: "same"). Super-recognizers' judgments clustered toward highly confident decisions. Examiners' judgments for same- and different-identities were symmetric across the scale midpoint (0); super-recognizers' judgments were not. Examiners showed higher identity judgment agreement than super-recognizers. Despite these qualitative differences, both groups showed insight into their own accuracy: more confident people and those who rated the task to be easier tended to be more accurate. Altogether, we show to better understand and interpret judgments according to the nature of someone's facial expertise, evaluations should assess more than accuracy.

Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms.

Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

Wisdom of the social versus non-social crowd in face identification.

Face identification is more accurate when people collaborate in social dyads than when they work alone (Dowsett & Burton, 2015, Br. J. Psychol., 106, 433). Identification accuracy is also increased when the responses of two people are averaged for each item to create a 'non-social' dyad (White, Burton, Kemp, & Jenkins, 2013, Appl. Cogn. Psychol., 27, 769; White et al., 2015, Proc. R. Soc. B Biol. Sci., 282, 20151292). Does social collaboration add to the benefits of response averaging for face identification? We compared individuals, social dyads, and non-social dyads on an unfamiliar face identity-matching test. We also simulated non-social collaborations for larger groups of people. Individuals and social dyads judged whether face image pairs depicted the same- or different identities, responding on a 5-point certainty scale. Non-social dyads were constructed by averaging the responses of paired individuals. Both social and non-social dyads were more accurate than individuals. There was no advantage for social over non-social dyads. For larger non-social groups, performance peaked at near perfection with a crowd size of eight participants. We tested three computational models of social collaboration and found that social dyad performance was predicted by the decision of the more accurate partner. We conclude that social interaction does not bolster accuracy for unfamiliar face identity matching in dyads beyond what can be achieved by averaging judgements.

Recognizing approaching walkers: Neural decoding of person familiarity in cortical areas responsive to faces, bodies, and biological motion.

In natural viewing environments, we recognize other people as they move through the world. Behavioral studies indicate that the face, body, and gait all contribute to recognition. We examined the neural basis of person recognition using a decoding approach aimed at discriminating the patterns of neural activity elicited in response to seeing visually familiar versus unfamiliar people in motion. Participants learned 30 identities by viewing multiple videos of the people in action. Recognition was tested inside a functional magnetic resonance imaging (fMRI) scanner using 8-s videos of 60 people (30 learned and 30 novel) approaching from a distance (~13m). Full brain images were taken while participants watched the approach. These images captured neural activity at four time points (TRs) corresponding to progressively closer views of the walker. We used pattern classification techniques to examine familiarity decoding in lateralized ROIs and the combination of left and right (bilateral) regions. Results showed accurate decoding of familiarity at the farthest distance in the bilateral posterior superior temporal sulcus (bpSTS). At a closer distance, familiarity was decoded in the bilateral extrastriate body area (bEBA) and left fusiform body area (lFBA). The most robust decoding was found in the time window during which the average behavioral recognition decision was made - and when the face came into clearer view. Multiple regions, including the right occipital face area (rOFA), bOFA, bFBA, bpSTS, and broadly distributed face- and body-selective voxels in the ventral temporal cortex decoded walker familiarity in this time window. At the closest distance, the lFBA decoded familiarity. These results reveal a broad system of ventral and dorsal visual areas that support person recognition from face, body, and gait. Although the face has been the focus of most person recognition studies, these findings remind us of the evolutionary advantage of being able to differentiate the people we know from strangers at a safe distance.

Creating Body Shapes From Verbal Descriptions by Linking Similarity Spaces.

Brief verbal descriptions of people's bodies (e.g., "curvy," "long-legged") can elicit vivid mental images. The ease with which these mental images are created belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and showed that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body-shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2,094 bodies. This relationship allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape that captures perceptually salient global and local body features.

Dissecting the time course of person recognition in natural viewing environments.

Person recognition often unfolds over time and distance as a person approaches, with the quality of identity information from faces, bodies, and motion in constant flux. Participants were familiarized with identities using close-up and distant videos. Recognition was tested with videos of people approaching from a distance. We varied the timing of prompted responses in the test videos, the amount of video seen, and whether the face, body, or whole person was visible. A free response condition was also included to allow participants to respond when they felt 'confident'. The pattern of accuracy across conditions indicated that recognition judgments were based on the most recently available information, with no contribution from qualitatively diverse and statistically useful person cues available earlier in the video. Body recognition was stable across viewing distance, whereas face recognition improved with proximity. The body made an independent contribution to recognition only at the farthest distance tested. Free response latencies indicated meta-knowledge of the optimal proximity for recognition from faces versus bodies. Notably, response bias varied strongly as a function of participants' expectation about whether closer proximity video was forthcoming. These findings lay the groundwork for developing person recognition theories that generalize to natural viewing environments.

Perceptual expertise in forensic facial image comparison.

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.