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• W2891820737 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." @default.
• W2891820737 created "2018-09-27" @default.
• W2891820737 creator A5061596476 @default.
• W2891820737 creator A5061684034 @default.
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• W2891820737 date "2019-01-01" @default.
• W2891820737 modified "2023-10-03" @default.
• W2891820737 title "Critical features for face recognition" @default.
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• W2891820737 doi "https://doi.org/10.1016/j.cognition.2018.09.002" @default.
• W2891820737 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30218914" @default.
• W2891820737 hasPublicationYear "2019" @default.
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