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Neural Comput. 2009 Mar;21(3):890-910. doi: 10.1162/neco.2008.07-07-566.

A bi-prototype theory of facial attractiveness.

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  • 1Information Science, Academia Sinica, Taipei, 115, Taiwan.


The attractiveness of human faces can be predicted with a high degree of accuracy if we represent the faces as feature vectors and compute their relative distances from two prototypes: the average of attractive faces and the average of unattractive faces. Moreover, the degree of attractiveness, defined in terms of the relative distance, exhibits a high degree of correlation with the average rating scores given by human assessors. These findings motivate a bi-prototype theory that relates facial attractiveness to the averages of attractive and unattractive faces rather than the average of all faces, as previously hypothesized by some researchers.

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