Abstract: The method of facial expression recognition based on static gray image which is simple and facile can get very high recognition ratio in person-dependent expression recognition，but the result of this method is failed in person-independent expression recognition because it is easy to be affected by some disturbances as complexion and illumination.The motion features extracted from dynamic expression sequence can effectively reflect the movement of expression and obtain better result in person-independent expression recognition.This paper expounds the researches of extracting two kinds of dynamic sequence features by optical flow and gray difference between frames and each kind of feature is combined with Support Vector Machines（SVM） and Hide Markov Models（HMM） in person-independent expression recognition.The characteristics and performances of the two features are also analyzed and the results show the validity of dynamic sequence features in facial expression recognition.