Bilinear decomposition for blended expressions representation

This paper proposes a new method for the analysis of blended expressions with varying intensity. The method is based on an asymmetric bilinear model learned on a small amount of expressions. In the resulting expression space, a blended unknown expression has a signature, that can be interpreted as a mixture of the basic expressions used in the creation of the space. Three methods are compared: a traditional method based on active appearance vectors, the asymmetric bilinear model on person-independent appearance vectors and the asymmetric bilinear model on person-specific appearance vectors. Experimental results on the recognition of 14 blended unknown expressions show the relevance of the bilinear models compared to appearance-based methods and the robustness of the person-specific models according to the types of parameters (shape and/or texture).