From concepts to percepts in human and machine face recognition: A reply to Blauch, Behrmann & Plaut

Galit Yovel, Naphtali Abudarham

Research output: Contribution to journalArticlepeer-review

Abstract

Intact recognition of familiar faces is critical for appropriate social interactions. Thus, the human face processing system should be optimized for familiar face recognition. Blauch et al. (2020) used face recognition deep convolutional neural networks (DCNNs) that are trained to maximize recognition of the trained (familiar) identities, to model human unfamiliar and familiar face recognition. In line with this model, we discuss behavioral, neuroimaging and computational findings that indicate that human face recognition develops from the generation of identity-specific concepts of familiar faces that are learned in a supervised manner, to the generation of view-invariant identity-general perceptual representations. Face-trained DCNNs seem to share some fundamental similarities with this framework.

Original languageEnglish
Article number104424
JournalCognition
Volume208
DOIs
StatePublished - Mar 2021

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