Abstract

Hyper-realistic masks present a new challenge to security and crime prevention. We have recently shown that people’s ability to differentiate these masks from real faces is extremely limited. Here we consider individual differences as a means to improve mask detection. Participants categorized single images as masks or real faces in a computer-based task. Experiment 1 revealed poor accuracy (40%) and large individual differences (5–100%) for high-realism masks among low-realism masks and real faces. Individual differences in mask categorization accuracy remained large when the Low-realism condition was eliminated (Experiment 2). Accuracy for mask images was not correlated with accuracy for real face images or with prior knowledge of hyper-realistic face masks. Image analysis revealed that mask and face stimuli were most strongly differentiated in the region below the eyes. Moreover, high-performing participants tracked the differential information in this area, but low-performing participants did not. Like other face tasks (e.g. identification), hyper-realistic mask detection gives rise to large individual differences in performance. Unlike many other face tasks, performance may be localized to a specific image cue.

Highlights

  • In a number of high-profile criminal cases, offenders have used hyper-realistic face masks (Fig. 1) to transform their facial appearance, leading police to pursue suspects who looked nothing like the actual offenders

  • The overarching aim is to establish whether an individual differences approach might be as useful in hyper-realistic mask detection as it has been in face identification

  • Group performance Real face images were correctly classified on 96.3% of trials and were not analyzed further

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Summary

Introduction

In a number of high-profile criminal cases, offenders have used hyper-realistic face masks (Fig. 1) to transform their facial appearance, leading police to pursue suspects who looked nothing like the actual offenders (e.g. different race or age; Bernstein, 2010). The present studies reveal large individual differences in the completely novel task of hyper-realistic mask detection and identify a specific region under the eyes that may drive accurate performance. The overarching aim is to establish whether an individual differences approach might be as useful in hyper-realistic mask detection as it has been in face identification.

Results
Conclusion

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