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Individual differences in hyper-realistic mask detection
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DOI：10.1186/s41235-018-0118-3.png)
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Abstract
En 中文
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.
Keywords:
Masks
Disguise
Face perception
Face detection
Face recognition
Deception
Fraud
Passports
Performance enhancement
Individual differences
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