Computational modeling of social face perception in humans: Leveraging the active appearance model

Abstract

Face processing plays a central role in human social life. Humans readily infer social traits (e.g. attractiveness and trustworthiness) from a strangers face. Previous attempts to characterize the facial (physiognomic) features underlying social processing have lacked either systematicity or interpretability. Here, we utilize a statistical framework to tackle this problem, by learning a vector space to represent faces, and a linear mapping from this face space into human social trait judgments. Specifically, we obtain a face space by training the Active Appearance Model on large datasets of face images. Based on human evaluations of numerous social traits on these images, we then use regression to find linear combinations of facial features (what we call Linear Trait Axis, or LTA) that best predict human social judgments. Our model achieves state-of-the-art performance in overall predictive accuracy - comparable to the best convolutional neural network and better than human prediction of other human ratings. To interpret the LTAs, we regress them against a large repertoire of geometric features. To understand the relationship between the facial features that underlie different social, emotional, and demographic traits, we present a novel \"dual space analysis\" that characterizes the geometric relationship among LTA vectors. It shows that facial features important for social trait perception are largely distinct from those underlying demographic and emotion perception, contrary to previous suggestions that social trait perception is driven by over-generalization of relatively primitive demographic and emotion perception processes. In addition, we present a novel correlation decomposition analysis that quantifies how correlations in trait judgments (e.g. between attractiveness and babyfacedness) independently arise from (1) shared facial features among traits, and (2) correlation in the distribution of facial features in the human population.