2D expression-invariant face recognition with constrained optical flow

Chao-Kuei Hsieh, Shang-Hong Lai, Yung-Chang Chen

Abstract: Face recognition is one of the most intensively studied topics in computer vision and pattern recognition. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intra-person optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.