Facial Attractiveness: Beauty and the Machine


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.

DOI: 10.1162/089976606774841602

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@article{Eisenthal2006FacialAB, title={Facial Attractiveness: Beauty and the Machine}, author={Yael Eisenthal and Gideon Dror and Eytan Ruppin}, journal={Neural computation}, year={2006}, volume={18 1}, pages={119-42} }