Facial expression recognition using multi-scale spatiotemporal local orientational pattern histogram projection in video sequences

Local binary pattern (LBP) is conceptually regarded as non-oriented, so it cannot capture sufficiently detailed information. Aiming at the problem, the local orientational pattern (LOP) method is proposed. It labels the pixels of the image by comparing two orientational differences at two neighboring pixels and encodes the change of the neighborhood orientational difference. Then LOP is extended to three-dimensional space, the spatiotemporal local orientational pattern (SLOP) is presented. The features obtained from three orthogonal planes are concatenated into a single vector. Finally the multi-scale SLOP histogram is used as face representation and projected onto locality preserving projection space to obtain lower-dimensional feature. Experimental results on Cohn-Kanade and MMI facial expression databases demonstrate that the proposed method outperforms other existing approaches in recognition rate and recognition speed. ©, 2015, Institute of Computing Technology. All right reserved.