ERIC Number: EJ1436675
Record Type: Journal
Publication Date: 2024-Jul
Pages: 13
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0888-4080
EISSN: EISSN-1099-0720
The Super-Recogniser Advantage Extends to the Detection of Hyper-Realistic Face Masks
Applied Cognitive Psychology, v38 n4 e4222 2024
Hyper-realistic silicone masks provide a viable route to identity fraud. Over the last decade, more than 40 known criminal acts have been committed by perpetrators using this type of disguise. With the increasing availability and bespoke sophistication of these masks, research must now focus on ways to enhance their detection. In this study, we investigate whether super-recognisers (SRs), people who excel at identity recognition, are more likely to detect this type of fraud, in comparison to typical-recogniser controls. Across three tasks, we examined mask detection rates in the absence of a pre-task prompt (covert task), and again after making participants aware of their use in criminal settings (explicit task). Finally, participants were asked to indicate which aspects of the masks could support their detection (regions of interest task). The findings show an SR advantage for the detection of hyper-realistic masks across the covert and explicit mask detection tasks. In addition, the eye, mouth, and nose regions appear to be particularly indicative of the presence of a mask. The lack of natural skin texture, proportional features, expressiveness, and asymmetry are also salient cues. The theoretical and applied implications of these findings are discussed.
Descriptors: Self Concept, Deception, Crime, Human Body, Clothing, Identification, Task Analysis, Individual Characteristics
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://bibliotheek.ehb.be:2191/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Data File: URL: https://osf.io/uhb7f/