ORCID Profile
0000-0001-8339-8832
Current Organisation
University of Lincoln
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Publisher: Wiley
Date: 30-03-2015
DOI: 10.1111/COGS.12231
Abstract: Research in face recognition has tended to focus on discriminating between in iduals, 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.
Publisher: Wiley
Date: 03-04-2019
DOI: 10.1111/BJOP.12388
Abstract: 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.
Publisher: Public Library of Science (PLoS)
Date: 13-10-2021
DOI: 10.1371/JOURNAL.PONE.0258241
Abstract: 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.
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.COGNITION.2019.04.027
Abstract: 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 in iduals. 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.
Publisher: Springer Science and Business Media LLC
Date: 29-07-2019
Publisher: Informa UK Limited
Date: 02-11-2017
Publisher: Wiley
Date: 15-12-2019
DOI: 10.1002/ACP.3620
Publisher: Springer Science and Business Media LLC
Date: 07-12-2016
DOI: 10.3758/S13428-016-0837-7
Abstract: We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the "face space" produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.
Publisher: Public Library of Science (PLoS)
Date: 17-08-2018
Publisher: Public Library of Science (PLoS)
Date: 22-03-2017
Publisher: Wiley
Date: 17-08-2018
DOI: 10.1002/ACP.3449
Publisher: Elsevier BV
Date: 03-2018
Publisher: American Psychological Association (APA)
Date: 03-2017
DOI: 10.1037/REV0000048
Publisher: Wiley
Date: 07-07-2023
DOI: 10.1002/ACP.4110
Abstract: It is becoming increasingly common for face morphs (weighted combinations of two people's photographs) to be submitted for inclusion in an official document, such as a passport. These images may sufficiently resemble both in iduals that they can be used by either person in a ‘fraudulently obtained genuine’ document. Problematically, people are poor at detecting face morphs and there is limited evidence that this can be improved. Here, we tested whether the ‘pairs training effect’ (working in pairs, which we know improves unfamiliar face matching) can improve face morph detection. We found morph detection was more accurate when working in a pair. Further, the lower performer in the pair maintained this benefit when completing the task again in idually. We conclude that the pairs training effect translates to face morph detection, and these findings have important implications for improving the detection of face morphs at the initial application stage.
Publisher: Public Library of Science (PLoS)
Date: 25-03-2015
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 10-04-2015
DOI: 10.1167/15.4.1
Abstract: 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.
Publisher: SAGE Publications
Date: 13-05-2019
Abstract: 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.
Publisher: SAGE Publications
Date: 03-02-2020
Abstract: Hyper-realistic face masks have been used as disguises in at least one border crossing and in numerous criminal cases. Experimental tests using these masks have shown that viewers accept them as real faces under a range of conditions. Here, we tested mask detection in a live identity verification task. Fifty-four visitors at the London Science Museum viewed a mask wearer at close range (2 m) as part of a mock passport check. They then answered a series of questions designed to assess mask detection, while the masked traveller was still in view. In the identity matching task, 8% of viewers accepted the mask as matching a real photo of someone else, and 82% accepted the match between masked person and masked photo. When asked if there was any reason to detain the traveller, only 13% of viewers mentioned a mask. A further 11% picked disguise from a list of suggested reasons. Even after reading about mask-related fraud, 10% of viewers judged that the traveller was not wearing a mask. Overall, mask detection was poor and was not predicted by unfamiliar face matching performance. We conclude that hyper-realistic face masks could go undetected during live identity checks.
Publisher: Elsevier BV
Date: 2018
DOI: 10.1016/J.COGNITION.2017.09.001
Abstract: 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 in idual (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 in idual. 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.
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222596
Abstract: Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
Publisher: SAGE Publications
Date: 13-08-2017
Abstract: As faces become familiar, we come to rely more on their internal features for recognition and matching tasks. Here, we assess whether this same pattern is also observed for a card sorting task. Participants sorted photos showing either the full face, only the internal features, or only the external features into multiple piles, one pile per identity. In Experiments 1 and 2, we showed the standard advantage for familiar faces—sorting was more accurate and showed very few errors in comparison with unfamiliar faces. However, for both familiar and unfamiliar faces, sorting was less accurate for external features and equivalent for internal and full faces. In Experiment 3, we asked whether external features can ever be used to make an accurate sort. Using familiar faces and instructions on the number of identities present, we nevertheless found worse performance for the external in comparison with the internal features, suggesting that less identity information was available in the former. Taken together, we show that full faces and internal features are similarly informative with regard to identity. In comparison, external features contain less identity information and produce worse card sorting performance. This research extends current thinking on the shift in focus, both in attention and importance, toward the internal features and away from the external features as familiarity with a face increases.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Robin Kramer.