Expression-Invariant Face Recognition With Constrained Optical Flow Warping

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 compute precise pixel displacements and intensity variations. By using the optical flow computed for the input expression variant face with respect to a reference neutral face image, we remove the expression from the face image by elastic image warping to recognize the subject with facial expression. Experimental validation is given to show that the proposed expression normalization algorithm significantly improves the accuracy of face recognition on expression variant faces.