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.
Eisenthal
Yael
Y
School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. yre2101@columbia.edu
Dror
Gideon
G
Ruppin
Eytan
E
D000465:Algorithms; D001185:Artificial Intelligence; D001504:Beauty; D004644:Emotions / Q000502:physiology; D005145:Face; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007858:Learning / Q000502:physiology*; D015588:Observer Variation; D010364:Pattern Recognition, Visual / Q000502:physiology*; D012725:Sexual Behavior / Q000523:psychology