Understanding 3D Emotions Through Compact Anthropometric Autoregressive Models

Reproducing realistic facial expressions is an important challenge in human computer interaction. In this paper we propose a novel method of modeling and recovering the transitions between different expressions through the use of an autoregressive process. In order to account for computational complexity, we adopt a compact face representation inspired from MPEG-4 standards while in terms of expressions a well known Facial Action Unit System (FACS) comprising the six dominant ones is considered. Then, transitions between expressions are modeled through a time series according to a linear model. Explicit constraints driven from face anthropometry and points interaction are inherited in this model and minimize the risk of producing non-realistic configurations. Towards optimal animation performance, a particular hardware architecture is used to provide the 3D depth information of the corresponding facial elements during the learning stage and the Random Sampling Consensus algorithm for the robust estimation of the model parameters. Promising experimental results demonstrate the potential of such an approach.