SVR-based facial texture driving for realistic expression synthesis

The facial texture variation is the key factor for realistic expression synthesis. It always changed with facial motion under some illumination. In this paper, we propose a realistic facial expression texture driving model based on the support vector regression and MPEG-4. It can learn and recall the regression relationship between facial animation parameters and the parameters of expression ratio image through support vector regression method. First, We can get the parameter set of expression ratio image and the eigenERI space by principle component analysis method, that generate reasonable ratio image. Then, a life-like facial animation can be synthesized quickly and effectively with the support vector regression mapping. In our experiment, it not only captures subtle changes in the variation illumination, but also can synthesis realistic facial expression in bad environment.