Unfamiliar Face Matching With Driving Licence and Passport Photographs

Face matching and self-insight A Registered Report investigating individual differences in metacognitive sensitivity, efficiency, and bias

Deciding whether two face photographs depict the same person or not can be a challenging task, and there are substantial individual differences in face matching ability. Far less is known about differences in metacognitive ability; that is, how well people can estimate the quality of their own face matching judgements. The purpose of this Registered Report was to determine the relationship between first-order performance in a face matching task, and three metacognitive measures: metacognitive sensitivity (the information exploited by metacognition), metacognitive efficiency (the quality of metacognitive processing itself), and metacognitive bias (the overall tendency towards high or low confidence). Participants completed a 200-trial unfamiliar face matching task, providing a second-order (metacognitive) confidence rating after each first-order (cognitive) response. Cognitive performance and metacognitive sensitivity showed a substantial positive association (rs = .60), suggesting that they were based on overlapping information, with poorer performers having lower quality information available. Once these differences in the information available to metacognition were accounted for, the quality of metacognitive processing itself (metacognitive efficiency) did not vary systematically with cognitive performance (rs = .00). Moreover, poor performers were not less or more confident overall than good performers, as cognitive performance and metacognitive bias did not correlate significantly (rs = .11). These findings allow us to move beyond consideration of group-level insight and inform regarding individual differences in cognitive and metacognitive abilities.

Metacognition during unfamiliar face matching

Kruger and Dunning (1999) described a metacognitive bias in which insight into performance is linked to competence: poorer performers are less aware of their mistakes than better performers. Competence-based insight has been argued to apply generally across task domains, including a recent report investigating social cognition using a variety of face-matching tasks. Problematically, serious statistical and methodological criticisms have been directed against the traditional method of analysis used by researchers in this field. Here, we further illustrate these issues and investigate new sources of insight within unfamiliar face matching. Over two experiments (total N = 1077), where Experiment 2 was a preregistered replication of the key findings from Experiment 1, we found that insight into performance was multi-faceted. Participants demonstrated insight which was not based on competence, in the form of accurate updating of estimated performance. We also found evidence of insight which was based on competence: the difference in confidence on correct versus incorrect trials increased with competence. By providing ways that we can move beyond problematic, traditional approaches, we have begun to reveal a more realistic story regarding the nature of insight into face perception.

Forensic Facial Comparison: Current Status, Limitations, and Future Directions

Global escalation of crime has necessitated the use of digital imagery to aid the identification of perpetrators. Forensic facial comparison (FFC) is increasingly employed, often relying on poor-quality images. In the absence of standardized criteria, especially in terms of video recordings, verification of the methodology is needed. This paper addresses aspects of FFC, discussing relevant terminology, investigating the validity and reliability of the FISWG morphological feature list using a new South African database, and advising on standards for CCTV equipment. Suboptimal conditions, including poor resolution, unfavorable angle of incidence, color, and lighting, affected the accuracy of FFC. Morphological analysis of photographs, standard CCTV, and eye-level CCTV showed improved performance in a strict iteration analysis, but not when using analogue CCTV images. Therefore, both strict and lenient iterations should be conducted, but FFC must be abandoned when a strict iteration performs worse than a lenient one. This threshold ought to be applied to the specific CCTV equipment to determine its utility. Chance-corrected accuracy was the most representative measure of accuracy, as opposed to the commonly used hit rate. While the use of automated systems is increasing, trained human observer-based morphological analysis, using the FISWG feature list and an Analysis, Comparison, Evaluation, and Verification (ACE-V) approach, should be the primary method of facial comparison.

The Importance of Internal and External Features in Matching Own and Other Race Faces

Research has shown that we are better at discriminating between faces that are our own race, and much less accurate with faces of another race. When the external features of faces were removed, this reduced the accuracy for recognizing other-races faces, more than own-race faces, suggesting that the external features (hair, face shape) are especially important for the recognition of other-race faces. The aim of the current study was to determine whether external features were more useful in matching other-race faces, and whether this was the case for Western and Eastern viewers. The current study employed a face matching task with Caucasian (U.K.) and Asian (Chinese) participants and found that responses were more accurate for own-race faces, and for whole faces when compared with faces where the internal or external features had been removed. Removing the external features of other-race faces increased the own-race bias for Chinese and U.K. participants, demonstrating the importance of viewing whole faces, including the external features when matching other-race faces.

Understanding the document bias in face matching

Matching unfamiliar faces is a well-studied task, apparently capturing important everyday decisions such as ID checks. In typical laboratory studies, participants make same/different judgements to pairs of faces, presented in isolation and without context. However, it has recently become clear that matching faces embedded in documents (e.g., passports and driving licences) induces a bias, resulting in elevated levels of “same person” responses. While practically important, it remains unclear whether this bias arises due to expectations induced by the ID cards or interference between textual information and faces. Here, we observe the same bias when faces are embedded in blank (i.e., non-authoritative) cards carrying basic personal information, but not when the same information is presented alongside a face without the card (Experiments 1 and 2). Cards bearing unreadable text (blurred or in an unfamiliar alphabet) do not induce the bias, but those bearing arbitrary (non-biographical) words do (Experiments 3 and 4). The results suggest a complex basis for the effect, relying on multiple factors which happen to converge in photo-ID.

Face Familiarity and Image-Specific Memory

Face familiarity produces advantages for both memory and matching. By developing an internal representation through repeated experience, viewers extract identity-specific information that aids subsequent recognition. However, researchers have recently argued that this process may also result in a familiarity disadvantage, whereby specific instances of the face are more difficult to remember, perhaps due to this process of prioritising identity- over image-specific information. Although previous experiments found no evidence of this disadvantage in working memory, initial research has demonstrated an effect in longer term storage. Here, we attempted to replicate this finding by focussing on the ability to learn images of a single (un)familiar identity. Our results failed to demonstrate a familiarity disadvantage while replicating the finding that familiarity influences response bias. As researchers continue to investigate how familiarity alters both internal representations and associated processes, it is important to establish which processes may or may not be affected.

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.