The psychometrics of rating facial attractiveness using different response scales.

Perceiving facial attractiveness is an important behaviour across psychological science due to these judgments having real-world consequences. However, there is little consensus on the measurement of this behaviour, and practices differ widely. Research typically asks participants to provide ratings of attractiveness across a multitude of different response scales, with little consideration of the psychometric properties of these scales. Here, we make psychometric comparisons across nine different response scales. Specifically, we analysed the psychometric properties of a binary response, a 0-100 scale, a visual analogue scale, and a set of Likert scales (1-3, 1-5, 1-7, 1-8, 1-9, 1-10) as tools to measure attractiveness, calculating a range of commonly used statistics for each. While certain properties suggested researchers might choose to favour the 1-5, 1-7 and 1-8 scales, we generally found little evidence of an advantage for one scale over any other. Taken together, our investigation provides consideration of currently used techniques for measuring facial attractiveness and makes recommendations for researchers in this field.

Multiple images captured from a single encounter do not promote face learning.

Viewing multiple images of a newly encountered face improves recognition of that identity in new instances. Studies examining face learning have presented high-variability (HV) images that incorporate changes that occur from moment-to-moment (e.g., head orientation and expression) and over time (e.g., lighting, hairstyle, and health). We examined whether low-variability (LV) images (i.e., images that incorporate only moment-to-moment changes) also promote generalisation of learning such that novel instances are recognised. Participants viewed a single image, six LV images, or six HV images of a target identity before being asked to recognise novel images of that identity in a face matching task (training stimuli remained visible) or a memory task (training stimuli were removed). In Experiment 1 (n = 71), participants indicated which image(s) in 8-image arrays belonged to the target identity. In Experiment 2 (n = 73), participants indicated whether sequentially presented images belonged to the target identity. Relative to the single-image condition, sensitivity to identity improved and response biases were less conservative in the HV condition; we found no evidence of generalisation of learning in the LV condition regardless of testing protocol. Our findings suggest that day-to-day variability in appearance plays an essential role in acquiring expertise with a novel face.

Face masks and fake masks: the effect of real and superimposed masks on face matching with super-recognisers, typical observers, and algorithms.

Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.

Not the norm: Face likeness is not the same as similarity to familiar face prototypes.

Face images depicting the same individual can differ substantially from one another. Ecological variation in pose, expression, lighting, and other sources of appearance variability complicates the recognition and matching of unfamiliar faces, but acquired familiarity leads to the ability to cope with these challenges. Among the many ways that face of the same individual can vary, some images are judged to be better likenesses of familiar individuals than others. Simply put, these images look more like the individual under consideration than others. But what does it mean for an image to be a better likeness than another? Does likeness entail typicality, or is it something distinct from this? We examined the relationship between the likeness of face images and the similarity of those images to average images of target individuals using a set of famous faces selected for reciprocal familiarity/unfamiliarity across US and UK participants. We found that though likeness judgments are correlated with similarity-to-prototype judgments made by both familiar and unfamiliar participants, this correlation was smaller than the correlation between similarity judgments made by different participant groups. This implies that while familiarity weakens the relationship between likeness and similarity-to-prototype judgments, it does not change similarity-to-prototype judgments to the same degree.

The pairs training effect in unfamiliar face matching.

A wealth of studies have shown that humans are remarkably poor at determining whether two face images show the same person or not (face matching). Given the prevalence of photo-ID, and the fact that people employed to check photo-ID are typically unfamiliar with the person pictured, there is a need to improve unfamiliar face matching accuracy. One method of improvement is to have participants complete the task in a pair, which results in subsequent improvements in the low performer ("the pairs training effect"). Here, we sought to replicate the original finding, to test the longevity of the pairs training effect, and to shed light on the potential underlying mechanisms. In two experiments, we replicated the pairs training effect and showed it is maintained after a delay (Experiment 1). We found no differences between high and low performers in confidence (Experiment 1) or response times (Experiment 2), and the content of the pairs' discussions (Experiment 2) did not explain the results. The pairs training effect in unfamiliar face matching is robust, but the mechanisms underlying the effects remain as yet unexplained.

Familiarity is familiarity is familiarity: Event-related brain potentials reveal qualitatively similar representations of personally familiar and famous faces.

Humans excel in familiar face recognition, but often find it hard to make identity judgements of unfamiliar faces. Understanding of the factors underlying the substantial benefits of familiarity is at present limited, but the effect is sometimes qualified by the way in which a face is known-for example, personal acquaintance sometimes gives rise to stronger familiarity effects than exposure through the media. Given the different quality of personal versus media knowledge, for example in one's emotional response or level of interaction, some have suggested qualitative differences between representations of people known personally or from media exposure. Alternatively, observed differences could reflect quantitative differences in the level of familiarity. We present 4 experiments investigating potential contributory influences to face familiarity effects in which observers view pictures showing their friends, favorite celebrities, celebrities they dislike, celebrities about whom they have expressed no opinion, and their own face. Using event-related potential indices with high temporal resolution and multiple highly varied everyday ambient images as a strong test of face recognition, we focus on the N250 and the later Sustained Familiarity Effect (SFE). All known faces show qualitatively similar responses relative to unfamiliar faces. Regardless of personal- or media-based familiarity, N250 reflects robust visual representations, successively refined over increasing exposure, while SFE appears to reflect the amount of identity-specific semantic information known about a person. These modulations of visual and semantic representations are consistent with face recognition models which emphasize the degree of familiarity but do not distinguish between different types of familiarity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Public attitudes towards the use of automatic facial recognition technology in criminal justice systems around the world.

Automatic facial recognition technology (AFR) is increasingly used in criminal justice systems around the world, yet to date there has not been an international survey of public attitudes toward its use. In Study 1, we ran focus groups in the UK, Australia and China (countries at different stages of adopting AFR) and in Study 2 we collected data from over 3,000 participants in the UK, Australia and the USA using a questionnaire investigating attitudes towards AFR use in criminal justice systems. Our results showed that although overall participants were aligned in their attitudes and reasoning behind them, there were some key differences across countries. People in the USA were more accepting of tracking citizens, more accepting of private companies' use of AFR, and less trusting of the police using AFR than people in the UK and Australia. Our results showed that support for the use of AFR depends greatly on what the technology is used for and who it is used by. We recommend vendors and users do more to explain AFR use, including details around accuracy and data protection. We also recommend that governments should set legal boundaries around the use of AFR in investigative and criminal justice settings.

The effect of face masks and sunglasses on identity and expression recognition with super-recognizers and typical observers.

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.

Multiple-image arrays in face matching tasks with and without memory.

Previous research has shown that exposure to within-person variability facilitates face learning. A different body of work has examined potential benefits of providing multiple images in face matching tasks. Viewers are asked to judge whether a target face matches a single face image (as when checking photo-ID) or multiple face images of the same person. The evidence here is less clear, with some studies finding a small multiple-image benefit, and others finding no advantage. In four experiments, we address this discrepancy in the benefits of multiple images from learning and matching studies. We show that multiple-image arrays only facilitate face matching when arrays precede targets. Unlike simultaneous face matching tasks, sequential matching and learning tasks involve memory and require abstraction of a stable representation of the face from the array, for subsequent comparison with a target. Our results show that benefits from multiple-image arrays occur only when this abstraction is required, and not when array and target images are available at once. These studies reconcile apparent differences between face learning and face matching and provide a theoretical framework for the study of within-person variability in face perception.

The importance of first impression judgements in interspecies interactions.

Close human-wildlife interactions are rapidly growing, particularly due to wildlife tourism popularity. Using both laboratory and ecological observation studies we explored potential interspecies communication signalling mechanisms underpinning human-animal approach behaviour, which to date have been unclear. First impression ratings (n = 227) of Barbary macaques' social and health traits were related to the macaques' facial morphology and their observed behaviour supporting a shared facial signalling system in primates. These ratings significantly predicted intended approach to the macaques during hypothetical interactions. Finally, real-world interspecies proximity was observed and found to be best predicted by the interaction between human first impression perception and animal behaviour. Specifically, perceived macaque health in interaction with actual macaque dominance drives close interactions despite human proclivity to avoid dominant animals, raising safety concerns in interspecies interactions.

Face averages and multiple images in a live matching task.

We know from previous research that unfamiliar face matching (determining whether two simultaneously presented images show the same person or not) is very error-prone. A small number of studies in laboratory settings have shown that the use of multiple images or a face average, rather than a single image, can improve face matching performance. Here, we tested 1,999 participants using four-image arrays and face averages in two separate live matching tasks. Matching a single image to a live person resulted in numerous errors (79.9% accuracy across both experiments), and neither multiple images (82.4% accuracy) nor face averages (76.9% accuracy) improved performance. These results are important when considering possible alterations which could be made to photo-ID. Although multiple images and face averages have produced measurable improvements in performance in recent laboratory studies, they do not produce benefits in a real-world live face matching context.

Face morphing attacks: Investigating detection with humans and computers.

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.

Familiarity does not inhibit image-specific encoding of faces.

When matching and recognizing familiar faces, performance is unaffected by changes to image-specific details such as lighting, head angle, and expression. In contrast, these changes have a substantial impact on performance when faces are unfamiliar. What process can account for this difference? Recent evidence shows a memory disadvantage for remembering specific images of familiar people compared to unfamiliar people, suggesting that image invariance in familiar face processing may be supported by loss of image-specific details in memory. Here, we examine whether this cost results from loss of image specific details during encoding of familiar faces. Participants completed four tasks that required participants to retain image-specific information in working memory: duplicate detection (Experiment 1), change detection (Experiment 2), short-term recognition memory (Experiment 3 and 5), and visual search (Experiment 4). Across all experiments (combined n = 270), our results consistently show equivalent memory performance for specific images of familiar and unfamiliar faces. We conclude that familiarity does not influence encoding of pictorial details, suggesting that loss of image-specificity reported in previous work is a result of longer-term storage mechanisms. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Making a Spectacle of Yourself: The Effect of Glasses and Sunglasses on Face Perception.

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.

Inter-rater agreement in trait judgements from faces.

Researchers have long been interested in how social evaluations are made based upon first impressions of faces. It is also important to consider the level of agreement we see in such evaluations across raters and what this may tell us. Typically, high levels of inter-rater agreement for facial judgements are reported, but the measures used may be misleading. At present, studies commonly report Cronbach's α as a way to quantify agreement, although problematically, there are various issues with the use of this measure. Most importantly, because researchers treat raters as items, Cronbach's α is inflated by larger sample sizes even when agreement between raters is fixed. Here, we considered several alternative measures and investigated whether these better discriminate between traits that were predicted to show low (parental resemblance), intermediate (attractiveness, dominance, trustworthiness), and high (age, gender) levels of agreement. Importantly, the level of inter-rater agreement has not previously been studied for many of these traits. In addition, we investigated whether familiar faces resulted in differing levels of agreement in comparison with unfamiliar faces. Our results suggest that alternative measures may prove more informative than Cronbach's α when determining how well raters agree in their judgements. Further, we found no apparent influence of familiarity on levels of agreement. Finally, we show that, like attractiveness, both trustworthiness and dominance show significant levels of private taste (personal or idiosyncratic rater perceptions), although shared taste (perceptions shared with other raters) explains similar levels of variance in people's perceptions. In conclusion, we recommend that researchers investigating social judgements of faces consider alternatives to Cronbach's α but should also be prepared to examine both the potential value and origin of private taste as these might prove informative.

What makes a face photo a 'good likeness'?

Photographs of people are commonly said to be 'good likenesses' or 'poor likenesses', and this is a concept that we readily understand. Despite this, there has been no systematic investigation of what makes an image a good likeness, or of which cognitive processes are involved in making such a judgement. In three experiments, we investigate likeness judgements for different types of images: natural images of film stars (Experiment 1), images of film stars from specific films (Experiment 2), and iconic images and face averages (Experiment 3). In all three experiments, participants rated images for likeness and completed speeded name verification tasks. We consistently show that participants are faster to identify images which they have previously rated asa good likeness compared to a poor likeness. We also consistently show that the more familiar we are with someone, the higher likeness rating we give to all images of them. A key finding is that our perception of likeness is idiosyncratic (Experiments 1 and 2), and can be tied to our specific experience of each individual (Experiment 2). We argue that likeness judgements require a comparison between the stimulus and our own representation of the person, and that this representation differs according to our prior experience with that individual. This has theoretical implications for our understanding of how we represent familiar people, and practical implications for how we go about selecting images for identity purposes such as photo-ID.

Forming impressions of facial attractiveness is mandatory.

First impressions of social traits, such as attractiveness, from faces are often claimed to be made automatically, given their speed and reliability. However, speed of processing is only one aspect of automaticity. Here we address a further aspect, asking whether impression formation is mandatory. Mandatory formation requires that impressions are formed about social traits even when this is task-irrelevant, and that once formed, these impressions are difficult to inhibit. In two experiments, participants learned what new people looked like for the purpose of future identification, from sets of images high or low in attractiveness. They then rated middle-attractiveness images of each person, for attractiveness. Even though instructed to rate the specific images, not the people, their ratings were biased by the attractiveness of the learned images. A third control experiment, with participants rating names, demonstrated that participants in Experiments 1 and 2 were not simply rating the people, rather than the specific images as instructed. These results show that the formation of attractiveness impressions from faces is mandatory, thus broadening the evidence for automaticity of facial impressions. The mandatory formation of impressions is likely to have an important impact in real-world situations such as online dating sites.

Learning faces from variability.

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.

Identity From Variation: Representations of Faces Derived From Multiple Instances.

Research in face recognition has tended to focus on discriminating between individuals, or "telling people apart." It has recently become clear that it is also necessary to understand how images of the same person can vary, or "telling people together." 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 person's 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 idiosyncratic-the dimensions of variability in one face do not generalize 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.

Viewers extract the mean from images of the same person: A route to face learning.

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.