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. 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. 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.
Models of social evaluation aim to capture the information people use to form first impressions of unfamiliar others. However, little is currently known about the relationship between perceived traits across gender. In Study 1, we asked viewers to provide ratings of key social dimensions (dominance, trustworthiness, etc.) for multiple images of 40 unfamiliar identities. We observed clear sex differences in the perception of dominance—with negative evaluations of high dominance in unfamiliar females but not males. In Study 2, we used the social evaluation context to investigate the key predictions about the importance of pictorial information in familiar and unfamiliar face processing. We compared the consistency of ratings attributed to different images of the same identities and demonstrated that ratings of images depicting the same familiar identity are more tightly clustered than those of unfamiliar identities. Such results imply a shift from image rating to person rating with increased familiarity, a finding which generalises results previously observed in studies of identification.
A paradoxical finding from recent studies of face perception is that observers are error-prone and inconsistent when judging the identity of unfamiliar faces, but nevertheless reasonably consistent when judging traits. Our aim is to understand this difference. Using everyday ambient images of faces, we show that visual image statistics can predict observers’ consensual impressions of trustworthiness, attractiveness and dominance, which represent key dimensions of evaluation in leading theoretical accounts of trait judgement. In Study 1, image statistics derived from ambient images of multiple face identities were able to account for 51% of the variance in consensual impressions of entirely novel ambient images. Shape properties were more effective predictors than surface properties, but a combination of both achieved the best results. In Study 2 and Study 3, statistics derived from multiple images of a particular face achieved the best generalisation to new images of that face, but there was nonetheless significant generalisation between images of the faces of different individuals. Hence, whereas idiosyncratic variability across different images of the same face is sufficient to cause substantial problems in judging the identities of unfamiliar faces, there are consistencies between faces which are sufficient to support (to some extent) consensual trait judgements. Furthermore, much of this consistency can be captured in simple operational models based on image statistics.
Learning new identities is crucial for effective social interaction. A critical aspect of this process is the integration of different images from the same face into a view-invariant representation that can be used for recognition. The representation of symmetrical viewpoints has been proposed to be a key computational step in achieving view-invariance. The aim of this study was to determine whether the representation of symmetrical viewpoints in face-selective regions is directly linked to the perception and recognition of face identity. In Experiment 1, we measured fMRI responses while male and female human participants viewed images of real faces from different viewpoints (−90, −45, 0, 45, and 90° from full-face view). Within the face regions, patterns of neural response to symmetrical views (−45 and 45° or −90 and 90°) were more similar than responses to nonsymmetrical views in the fusiform face area and superior temporal sulcus, but not in the occipital face area. In Experiment 2, participants made perceptual similarity judgements to pairs of face images. Images with symmetrical viewpoints were reported as being more similar than nonsymmetric views. In Experiment 3, we asked whether symmetrical views also convey an advantage when learning new faces. We found that recognition was best when participants were tested with novel face images that were symmetrical to the learning viewpoint. Critically, the pattern of perceptual similarity and recognition across different viewpoints predicted the pattern of neural response in face-selective regions. Together, our results provide support for the functional value of symmetry as an intermediate step in generating view-invariant representations.
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.
Matching two different images of an unfamiliar face is difficult, although we rely on this process every day when proving our identity. Although previous work with laboratory photosets has shown that performance is error-prone, few studies have focussed on how accurately people carry out this matching task using photographs taken from official forms of identification. In Experiment 1, participants matched high-resolution, colour face photos with current UK driving licence photos of the same group of people in a sorting task. Averaging 19 mistaken pairings out of 30, our results showed that this task was both difficult and error-prone. In Experiment 2, high-resolution photographs were paired with either driving licence or passport photographs in a typical pairwise matching paradigm. We found no difference in performance levels for the two types of ID image, with both producing unacceptable levels of accuracy (around 75%–79% correct). The current work benefits from increased ecological validity and provides a clear demonstration that these forms of official identification are ineffective and alternatives should be considered.
A growing body of research has investigated how we associate colours and social traits. Specifically, studies have explored the links between red and perceptions of qualities like attractiveness and anger. Although less is known about other colours, the prevailing framework suggests that the specific context plays a significant role in determining how a particular colour might affect our perceptions of a person or item. Importantly, this factor has yet to be considered for children’s colour associations, where researchers focused on links between colours and emotions, rather than social traits. Here, we consider whether context-specific colour associations are demonstrated by 5- to 10-year-old children and compare these associations with adult data collected on the same task. We asked participants to rank order sets of six identical images (e.g., a boy completing a test), which varied only in the colour of a single item (his T-shirt). Each question was tailored to the image set to address a specific context, for example, “Which boy do you think looks the most likely to cheat on a test?” Our findings revealed several colour associations shared by children, and many of these were also present in adults, although some had strengthened or weakened by this stage of life. Taken together, our results demonstrate the presence of both stable and changing context-specific colour associations during development, revealing a new area of study for further exploration.
Little is known about transgender women’s beliefs and experiences of hormone therapy (HT), as part of their transition process, and particularly as they grow older. Aims: This study aimed to investigate: (i) transgender women’s experiences and attitudes to HT, and (ii) expectations of what might occur and/or what occurred after they reached “menopausal age.” Participants were recruited through invitations to an online survey sent to 138 Lesbian, gay, bisexual, transgender plus (LGBT+) support groups across the UK. Sixty-seven transgender women consented and completed the questionnaire; responses were analyzed using a mixed-methods approach. The beliefs about medicines questionnaire (BMQ) was used to assess beliefs about HT, while an inductive thematic qualitative approach was used to explore participants’ personal expectations and experiences of HT and their views about the menopause. Participants were aged on average 49 years ranging from 20 to 79 years old. Most (96%) were taking HT. BMQ scores revealed strong beliefs about the necessity of HT and some concerns. Positive views about HT were expressed, with themes including treatment importance, personal and mental health benefits, but concerns about long-term effects, side effects, and maintaining access to the treatment were also mentioned. Views about menopause included uncertainty and questioning of its relevance; some mentioned changes to HT dosage, but most expected to use HT indefinitely. This study provides exploratory qualitative and quantitative information about transgender women’s views about HT and menopause. Practical implications include improving access to HT and provision of evidence-based information about long-term use.
Low‐quality images are problematic for face identification, for example, when the police identify faces from CCTV images. Here, we test whether face averages, comprising multiple poor‐quality images, can improve both human and computer recognition. We created averages from multiple pixelated or nonpixelated images and compared accuracy using these images and exemplars. To provide a broad assessment of the potential benefits of this method, we tested human observers (n = 88; Experiment 1), and also computer recognition, using a smartphone application (Experiment 2) and a commercial one‐to‐many face recognition system used in forensic settings (Experiment 3). The third experiment used large image databases of 900 ambient images and 7,980 passport images. In all three experiments, we found a substantial increase in performance by averaging multiple pixelated images of a person’s face. These results have implications for forensic settings in which faces are identified from poor‐quality images, such as CCTV.
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.