Learning Facial Attractiveness

In this work we study of the notion of "attractiveness" of faces in a machine-learning context. To this end, we collected human beauty ratings for datasets of facial images and used various techniques for learning the average attractiveness of a face. The results clearly show that beauty is a universal concept, which can be learned by a machine. Due to the limited size of the dataset, most of the information about the target is extracted from features that are simply correlated with facial beauty