In real-time facial expression recognition, accurate and fast face tracking is a very important preparatory part to obtain the image sequences of facial expressions. For this problem, an improved mean shift algorithm is proposed for real-time face tracking. Facial expression image sequences are obtained with the method. The method is based on using pixel gray value distribution as the feature as well as combination of the density distribution of the objective gradient direction. Alternating iterative operations can be carried through the iterative formula of these two features, and thus, we can make the human face rotation and translation movement tracking better. Then we used the geometric model based on human face to locate the region of facial expression features, and estimate the optical flow to calculate the Eigen-flow vectors. Finally, hidden semi-Markov model is used for facial expression recognitions. Experiments show that the proposed method can effectively track the face under rotation and translation movement of the head and it is very effective to obtain the facial expression image sequences quickly and accurately.
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