Research in face recognition has tended to focus on discriminating between individuals, or Ôtelling people apartÕ. It has recently become clear that it is also necessary to understand how images of the same person can vary, or Ôtelling people togetherÕ. Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that personÕs face. Here we present an application of Principal Components Analysis computed across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncraticÑthe dimensions of variability in one face do not generalise well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal, and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework.

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.

Research on face learning has tended to use sets of images that vary systematically on dimensions such as pose and illumination. In contrast, we have proposed that exposure to naturally varying images of a person may be a critical part of the familiarization process. Here, we present two experiments investigating face learning with “ambient images”—relatively unconstrained photos taken from internet searches. Participants learned name and face associations for unfamiliar identities presented in high or low within-person variability—that is, images of the same person returned by internet search on their name (high variability) versus different images of the same person taken from the same event (low variability). In Experiment 1 we show more accurate performance on a speeded name verification task for identities learned in high than in low variability, when the test images are completely novel photos. In Experiment 2 we show more accurate performance on a face matching task for identities previously learned in high than in low variability. The results show that exposure to a large range of within-person variability leads to enhanced learning of new identities.

Over the last ten years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgments of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries, and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods, correlate and rotate the dimension reduction solution.

Research on ensemble encoding has found that viewers extract summary information from sets of similar items. When shown a set of four faces of different people, viewers merge identity information from the exemplars into a representation of the set average. Here, we presented sets containing unconstrained images of the same identity. In response to a subsequent probe, viewers recognized the exemplars accurately. However, they also reported having seen a merged average of these images. Importantly, viewers reported seeing the matching average of the set (the average of the four presented images) more often than a nonmatching average (an average of four other images of the same identity). These results were consistent for both simultaneous and sequential presentation of the sets. Our findings support previous research suggesting that viewers form representations of both the exemplars and the set average. Given the unconstrained nature of the photographs, we also provide further evidence that the average representation is invariant to several high-level characteristics.

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Background In recent years, fraudsters have begun to use readily accessible digital manipulation techniques in order to carry out face morphing attacks. By submitting a morph image (a 50/50 average of two people’s faces) for inclusion in an official document such as a passport, it might be possible that both people sufficiently resemble the morph that they are each able to use the resulting genuine ID document. Limited research with low-quality morphs has shown that human detection rates were poor but that training methods can improve performance. Here, we investigate human and computer performance with high-quality morphs, comparable with those expected to be used by criminals. Results Over four experiments, we found that people were highly error-prone when detecting morphs and that training did not produce improvements. In a live matching task, morphs were accepted at levels suggesting they represent a significant concern for security agencies and detection was again error-prone. Finally, we found that a simple computer model outperformed our human participants. Conclusions Taken together, these results reinforce the idea that advanced computational techniques could prove more reliable than training people when fighting these types of morphing attacks. Our findings have important implications for security authorities worldwide. Electronic supplementary material The online version of this article (10.1186/s41235-019-0181-4) contains supplementary material, which is available to authorized users.

We investigated the effect of wearing glasses and sunglasses on the perception of social traits from faces and on face matching. Participants rated images of people wearing no glasses, glasses and sunglasses on three social traits (trustworthiness, competence and attractiveness). Wearing sunglasses reduced ratings of trustworthiness. Participants also performed a matching task (telling whether two images show the same person or not) with pairs of images both wearing no glasses, glasses or sunglasses, and all combinations of eyewear. Incongruent eyewear conditions (e.g., one image wearing glasses and the other wearing sunglasses, etc.) reduced performance. Further analysis comparing performance on congruent and incongruent eyewear trials showed that our effects were driven by match trial performance, where differences in eyewear decreased accuracy. For same-eyewear-condition pairs, performance was poorer for pairs of images both wearing sunglasses than no glasses. Our results extend and update previous research on the effect of eyewear on face perception.