This paper introduces a new video-based facial expression recognition system. Facial
expression analysis encounters two major problems: non-rigid shape deformation and
person-specific facial expression appearance. Our method analyzes the video sequence
to recognize facial expression and locate the temporal apex of the facial expression by
using modified Hough forest and minimizing the influence of person-specific facial
expression appearance. Our contributions are (1) random sampling 3-D accumulated
spatial-temporal motion map to generate video patches, (2) proposing the correlation
filtering for more effective Hough voting, and (3) recognizing and locating the apex of
the facial expression. The experimental results show that the performance of our
method is better than the other face expression recognition methods.
