Appearance factorization for facial expression analysis

This paper addresses the issue of face representations for facial expression analysis and synthesis. In this context, a global appearance model is used and two bilinear factorization models are subsequently proposed to separate expression and identity factors from the global appearance parameters. A feature extraction technique inspired from the above representations is then proposed which consists in automatically computing the optimal identity and expression components that best adapt to an unknown target face. The proposed representation can be seen as an alternative to the costly AAM gradient matrix construction and iterative search and is exploited in the context of facial expression control. Results are compared with the ones obtained using bilinear factorization and linear regression in the space of AAM parameters.