Physiognomy: Personality traits prediction by learning

Abstract

Evaluating individuals’ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits (also measured intelligence) and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points: 1) “Rule-consciousness” and “Tension” can be reliably predicted from face images. 2) It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3) Convolutional neural network (CNN) features outperform traditional handcrafted features in predicting traits.