Block-diagonal projective representation for face recognition

Most feature representation algorithms are susceptible to noise when mining the internal structure of the high-dimensional data. Meanwhile, their feature learning and classifier design are separated, resulting in the limited classification performance in practice. Aimed at this issue, a new feature representation method, Block-Diagonal Projective Representation (BDPR), is proposed in this paper. First, a weighted matrix is imposed on the coding coefficients of samples over each class. By using such local constraints to enhance the similarity between the coefficients and reduce the impact of noise on coefficient learning, the proposed BDPR can well maintain the internal data structure. Second, to closely correlate the data with their coding coefficients and reduce the difficulty of learning the representation coefficients, we construct a block-diagonal constraint to learn a discriminative projection. In this way, the sample representation coefficients can be obtained in the low-dimensional projected subspace, which contains more global structure information between samples and enjoys lower computational complexity. Finally, the representation learning and classifier learning are integrated into the same framework. By increasing the "label distance" between samples of different classes, BDPR updates the discriminative projection and classifier in an iterative manner. In this way, the most suitable classifier can be found for the current optimal feature representation, making the proposed algorithm automatically realize the classification task. The results of experiments on multiple benchmark face datasets show that BDPR has achieved better recognition performance, compared to traditional collaborative representation based classification and several mainstream subspace learning algorithms.