Personalized facial attractiveness prediction

We present a fully automatic approach to learning the personal facial attractiveness preferences of individual users directly from example images. The target application is computer assisted search of partners in online dating services. The proposed approach is based on the use of epsiv-SVMs to learn a regression function that maps low level image features onto attractiveness ratings. We present empirical results based on a dataset of images collected from a large online dating site. Our system achieved correlations of up to 0.45 (Pearson correlation) on the attractiveness predictions for individual users. We show evidence that the approach learned not just a universal sense of attraction shared by multiple users, but capitalized on the preferences of individual subjects. Our results are promising and could already be used to facilitate the personalized search of partners in online dating.