Projective Representation Learning for Discriminative Face Recognition

Face recognition is a challenging issue due to various appearances under different conditions of the face of a person. Meanwhile, conventional face representation methods always lead to high computational complexity. To overcome these shortcomings, in this paper, we propose a novel discriminative projection and representation method for face recognition. This method tries to seek a discriminative representation of the face image on a low-dimension space. Our method consists of two stages, namely face projection and face representation. In the face projection stage, a mapping matrix is produced by jointly maximizing the covariance of dissimilar samples and minimizing the covariance of similar samples. In the face representation stage, the representation result for each face image is obtained by minimizing the sum of representation results of each class. The proposed method achieves two-fold discriminative properties and provides a computational efficient algorithm. The experiments evaluated on diverse face datasets demonstrate that the proposed method has great superiority for face recognition task.