Individual differences in hyper-realistic mask detection

• Published in: Cognitive research: principles and implications Vol. 3; no. 1; p. 24
• Main Authors: Sanders, Jet G., Jenkins, Rob
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 27-06-2018; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Behavioral Science and Psychology; Cognitive Psychology; Crime prevention; Deception; Disguise; Experimental Psychology; Face; Face detection; Face perception; Face recognition; Image processing; Individual Differences in Face Perception and Person Recognition; Masks; Neurosciences; Original; Original Article; Psychology; Realism; Disguise; Face perception; Face recognition; Deception; Individual differences; Performance enhancement; Fraud; Face detection; Passports; Masks
• ISSN: 2365-7464; 2365-7464
• DOI: 10.1186/s41235-018-0118-3

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.