Too good to be true

Synthetic AI faces are more average than real faces and super‐recognizers know it

Bibliographic Data

ID21297912
AuthorsJames D Dunn (0000-0002-6964-909X, School of Psychology UNSW Sydney Sydney New South Wales Australia, corresponding author), David White (0000-0002-6366-2699, School of Psychology UNSW Sydney Sydney New South Wales Australia), Clare A M Sutherland (0000-0003-0443-3412, School of Psychology King's College, University of Aberdeen Aberdeen UK), Elizabeth J Miller (0000-0003-2572-6134, School of Medicine and Psychology The Australian National University Canberra Australian Capital Territory Australia), Ben A Steward (0000-0002-7517-9215, School of Medicine and Psychology The Australian National University Canberra Australian Capital Territory Australia), Amy Dawel (0000-0001-6668-3121, School of Medicine and Psychology The Australian National University Canberra Australian Capital Territory Australia)
Year2026
Publication date2026-02-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBritish Journal of Psychology (JOURNAL)
Journal identifiersISSN: 0007-1269 • E-ISSN: 2044-8295
PublisherWiley (PUBLISHER • GB)
DOI10.1111/bjop.70063
PMID41705896
OpenAlexW7130410624
LanguageEN
References cited59

The AI revolution has produced synthetic faces that often appear more human than photos of real people. We tested whether individual differences in human face recognition ability explain variation in discriminating AI from real faces. Super‐recognizers – people with exceptional ability to recognize human faces ( N = 36) – outperformed a typical sample by 15% and by 7% compared to a group of higher performing, motivated control participants (Cohen's d = 0.55; N = 89). Individual difference analysis revealed that this pattern reflected a positive association between human face recognition and AI face discrimination abilities. AI discrimination ability was also associated with individuals' sensitivity to the ‘hyper‐average’ appearance of AI faces. Deep neural networks optimized for face identity processing confirmed a more central distribution of AI faces in face‐space. Moreover, centrality was associated with a higher probability of super‐recognizers judging the faces as AI, but this pattern was not observed for controls. Super‐recognizers' correct interpretation of hyper‐averageness as a cue to artificiality constitutes the first mechanistic link between evolved expertise in face processing and AI face detection and addresses a common misconception regarding the structure of human face space

Artificial neural network · Artificiality · Face (sociological concept) · Face perception · Facial recognition system · Identity (music) · Pattern recognition (psychology) · Evolutionary Psychology and Human Behavior · Face recognition and analysis · Face Recognition and Perception

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