Facial Attractiveness: Beauty and the Machine

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.

DOI: 10.1162/089976606774841602

Extracted Key Phrases

10 Figures and Tables

02040'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

234 Citations

Semantic Scholar estimates that this publication has 234 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@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} }