Fusing landmark-based features at kernel level for face recognition

Because of the dramatic intra-class variations in lighting, expression and pose of face images, no single feature is rich enough to capture all the discriminant information, fusing multiple features is an efficient way to improve performance for face recognition. But most of the existing fusing methods use features sampling at fixed gird and manually set too many parameters, thus their performances are limited. In this paper, we first propose an improved landmark-based multi-scale LBP feature to address the dramatic pose and expression variations, which samples features around landmarks instead of fixed grid. Then we propose a novel model which fuses LBP feature and Gabor feature at kernel-level to capture the information of facial texture and facial shape, where the weighted coefficients between kernels, the discriminant projection matrix and the standard deviations of RBF kernel are simultaneously learnt by the proposed optimization algorithm. Experiments are done on LFW, AR and Extended Yale B datasets, and results show that not only does the proposed method get much better identification performance than some state-of-the-art methods, but it also achieves competitive result for verification task.