Psychologically Valid Social Face Features for Virtual Agents

Abstract

Virtual agents with social characteristics such as warmth and competence, which are perceived readily from the face, impact human behavior effectively. However, generating such social signals is challenging because faces are complex, high-dimensional information spaces of 3D shape and complexion. We address this challenge using a data-driven psychology-based method that can objectively model socially impactful face features from subjective human perception. Specifically, we modeled the 3D shape and 2D complexion face features that drive four key social perceptions-competence, dominance, warmth, and trustworthiness. Analysis of the face models revealed that the four key traits are systematically structured by a set of latent face features including eyebrow height and chin protrusion, which can thus be used to efficiently generate social trait face features in virtual agents. We anticipate that our psychologically valid generative model of social face signals will enhance the social signaling capabilities of virtual agents.