Critical features for face recognition

Naphtali Abudarham, Lior Shkiller, Galit Yovel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Face recognition is a computationally challenging task that humans perform effortlessly. Nonetheless, this remarkable ability is better for familiar faces than unfamiliar faces. To account for humans’ superior ability to recognize familiar faces, current theories suggest that different features are used for the representation of familiar and unfamiliar faces. In the current study, we applied a reverse engineering approach to reveal which facial features are critical for familiar face recognition. In contrast to current views, we discovered that the same subset of features that are used for matching unfamiliar faces, are also used for matching as well as recognition of familiar faces. We further show that these features are also used by a deep neural network face recognition algorithm. We therefore propose a new framework that assumes similar perceptual representation for all faces and integrates cognition and perception to account for humans’ superior recognition of familiar faces.

Original languageEnglish
Pages (from-to)73-83
Number of pages11
JournalCognition
Volume182
DOIs
StatePublished - Jan 2019

Funding

FundersFunder number
DOD Counterdrug Technology Development Program Office

    Keywords

    • Deep neural network
    • Face recognition
    • Face space
    • Familiar faces
    • Feature space

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