Building a Generative Space of Facial Expressions of Emotions Using Psychological Data-driven Methods

Abstract

To engage their human users, socially interactive virtual agents must be equipped with the ability to communicate emotions using facial expressions. Therefore, a main goal is to build a generative model that can produce the range of realistic dynamic facial expressions of emotion that occur across social life. We contribute to this goal by building a psychologically valid generative model of facial expressions directly from subjective human perception using a novel psychology-based approach. First, we build a valence-arousal space of face movements by identifying the specific face movements that convey valence (positive/negative) and arousal (excited/calm) in 40 individual participants. We then tested the capacity of the valence-arousal space to generate a broad range of facial expressions of emotion including the six classic emotions and complex emotions. By cross-correlating a large set of facial expressions of basic and complex emotions with the valence-arousal space, we show that our model can successfully represent a wide range of emotions. We anticipate that our psychologically valid facial expression generation model will enhance the emotion signalling capabilities of virtual agents.