Understanding face identification through within-person variability in appearance: Introduction to a virtual special issue

Unfamiliar face matching ability predicts the slope of face learning

We provide the first examination of individual differences in the efficiency of face learning. Investigating individual differences in face learning can illuminate potential mechanisms and provide greater understanding of why certain individuals might be more efficient face learners. Participants completed two unfamiliar face matching tasks and a learning task in which learning was assessed after viewing 1, 3, 6, and 9 images of to-be-learned identities. Individual differences in the slope of face learning (i.e., increases in sensitivity to identity) were predicted by the ability to discriminate between matched (same-identity) vs. mismatched (different-identity) pairs of wholly unfamiliar faces. A Dual Process Signal Detection model showed that three parameters increased with learning: Familiarity (an unconscious type of memory that varies in strength), recollection-old (conscious recognition of a learned identity), and recollection-new (conscious/confident rejection of novel identities). Good (vs. poor) matchers had higher Recollection-Old scores throughout learning and showed a steeper increase in Recollection-New. We conclude that good matchers are better able to capitalize on exposure to within-person variability in appearance, an effect that is attributable to their conscious memory for both learned and novel faces. These results have applied implications and will inform contemporary and traditional models of face identification.

Improving face identification of mask-wearing individuals

Research has consistently shown that concealing facial features can hinder subsequent identification. The widespread adoption of face masks due to the COVID-19 pandemic has highlighted the critical and urgent need to discover techniques to improve identification of people wearing face coverings. Despite years of research on face recognition and eyewitness identifications, there are currently no evidence-based recommendations for lineup construction for cases involving masked individuals. The purpose of this study was to examine identification accuracy of a masked perpetrator as a function of lineup type (i.e., unmasked or masked lineups) and perpetrator presence (i.e., absent or present). In both experiments, discriminability was superior for masked lineups, a result that was due almost exclusively to higher hits rates in target-present conditions. These data suggest that presenting a masked lineup can enhance identification of masked faces, and they have important implications for both eyewitness identification and everyday face recognition of people with face coverings.