Abstract
This paper describes a method for recognition of continuous facial expression change in video sequences. ASM automatically localizes the facial feature points in the first frame and then tracks the feature points through the video frames. After that the step is the selection of the 20 optimal key facial points, those which change the most with changes in expression. Since the distance of geometric features, a set of displacement vectors, is of a high dimensions, it is mapped into a low dimensional space, called feature space, by applying PCA expansion. Then estimation of input image is achieved by projecting it on to the feature space. After build the feature space, we trained SVM classification and tested it for result.
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