Real-time estimation of facial expression intensity

Abstract

Changing facial expressions is a natural and powerful way of conveying personal intention, expressing emotion and regulating interpersonal communication. Automatic estimation of human facial expression intensity is an important step in enhancing the capability of human-robot interfaces. In this research, we have developed a system which can automatically estimate the intensity of facial expression in real-time. Based on isometric feature mapping, the intensity of expression is extracted from training facial transition sequences. Then, intelligent models including cascade neural networks and support vector machines are applied to model the relationship between the trajectories of facial feature points and expression intensity level. We have implemented a vision system which can estimate the expression intensity of happiness, anger and sadness in real-time.