Learning Discriminant Face Descriptor for Face Recognition

Face descriptor is a critical issue for face recognition. Many local face descriptors like Gabor, LBP have exhibited good discriminative ability for face recognition. However, most existing face descriptors are designed in a handcrafted way and the extracted features may not be optimal for face representation and recognition. In this paper, we propose a learning based mechanism to learn the discriminant face descriptor (DFD) optimal for face recognition in a data-driven way. In particular, the discriminant image filters and the optimal weight assignments of neighboring pixels are learned simultaneously to enhance the discriminative ability of the descriptor. In this way, more useful information is extracted and the face recognition performance is improved. Extensive experiments on FERET, CAS-PEAL-R1 and LFW face databases validate the effectiveness and good generalizations of the proposed method.