Abstract

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize expressional faces with one single training sample per class. In this paper, we modify the regularization-based optical flow algorithm by imposing constraints on some given point correspondences to obtain precise pixel displacements and intensity variations. By using the optical flow computed for the input expressional face with respect to a referenced neutral face, we remove the expression from the face image by elastic image warping to recognize the subject with facial expression. Numerical validations of the proposed method are given, and experimental results show that the proposed method improves the recognition rate significantly.

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