Data-driven studies in face identity processing rely on the quality of the tests and data sets

Bobak, Anna K.; Jones, Alex L.; Hilker, Zoe; Mestry, Natalie; Bate, Sarah; Hancock, Peter J.B.

doi:10.1016/j.cortex.2023.05.018

Cited by 5 publications

(1 citation statement)

References 58 publications

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“…Moreover, face matching performance will vary between individual observers, due to stable, inter-individual di erences in face identity processing (FIP; [44,45,46,47,48]). Overall, the same external conditions a ect individuals di erently, depending on their respective location on the FIP ability spectrum [49].…”

Section: Determinants Of Natural Face Discriminationmentioning

confidence: 99%

Synthetic And Natural Face Identity Processing Share Common Mechanisms

Uittenhove,

Otroshi Shahreza,

Marcel

et al. 2024

Preprint

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Recent developments in generative AI offer the means to create synthetic identities, or deepfakes, at scale. As deepfake faces and voices become indistinguishable from real ones, they are considered as promising alternatives for research and development to enhance fairness and protect humans’ rights to privacy. Notwithstanding these efforts and intentions, a basic question remains unanswered: Are natural faces and facial deepfakes perceived and remembered in the same way? Using images created via professional photography on the one hand, and a state-of-the-art generative model on the other, we investigated the most studied process of face cognition: perceptual matching and discrimination of facial identity. Our results demonstrate that identity discrimination of naturalandsynthetic faces is governed by the same underlying perceptual mechanisms: objective stimulus similarity and observers’ ability level. These findings provide empirical support both for the societal risks associated with deepfakes, while also underscoring the utility of synthetic identities for research and development.

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