Stepwise Correlation metric based Discriminant Analysis and multi-probe images fusion for face recognition

Face recognition is a great challenge in practice. Subspace learning method is one of the dominant methods and has achieved great success in face recognition area. In subspace learning, many researches have found that correlation similarity (e.g. cosine distance) usually achieves better classification results than L2 distance with nearest neighbor (NN) classifier in Euclidean space. However, in traditional methods, most of them are devoted to optimize the objective function based on L2 distance, which is not coincident with the classification rule. It is reasonable to obtain better results by optimizing the objective function with correlation metric directly. In this paper, following traditional linear discriminant analysis (LDA), we redefine the between and with-in class scatter with correlation metric and propose an efficient Stepwise Correlation metric based Discriminant Analysis (SCDA) method to derive the sub-optimal discriminant subspace to be classified with correlation similarity. Moreover, we propose a novel weighted fusion mechanism to learn the optimal combination of multi-probe images to be classified. Extensive experiments on PIE and extended Yale-B databases validate the effectiveness of SCDA and the learning based weighted image fusion method.