Facial expressive image analysis by using nonlinear factorization model

Facial images captured by camera are usually influenced by several different kinds of factor, such as human identity, facial expression, and etc. Therefore facial expressive image analysis plays an important role in many different applications, such as expression recognition, face recognition with expressive images, facial expressive image synthesis and etc. In this paper, "human identity" and "facial expression" are regarded as two influence factors of face appearance, and a novel kernel-based bilinear factorization method is proposed to decouple the interaction from each other. The trained non-linear factorization model has been used for facial expressive image translation, and to person and/or expression recognition, and got promising results. In this paper, the principle of our method is described and some preliminary experimental results are included.