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Facial Attractiveness: Beauty and the Machine

yre2101{at}columbia.edu, School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel

Gideon Dror

gideon{at}mta.ac.il, Department of Computer Science, Academic College of Tel-Aviv-Yaffo, Tel-Aviv 64044, Israel

Eytan Ruppin

ruppin{at}post.tau.ac.il, School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel

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.