Abstract
Most of the existing studies on 3D Facial Expression Recognition (FER) are messaged-based approaches, which only detect the already known six universal expressions. In this paper, we describe the group of global and local features used to comprehensively characterize facial activities. These features are further used to train Statistical Feature Models (SFMs) associated with each Action Unit (AU). The occurrence probability of a specific AU on an input textured 3D face model is then computed. The results demonstrate that the evidence of AUs is of importance for applying AU space to evaluate expressions.
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