Facial attractiveness: Beauty and the machine

  • Eisenthal Y
  • Dror G
  • Ruppin E
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Abstract

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

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Authors

  • Yael Eisenthal

  • Gideon Dror

  • Eytan Ruppin

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