Enhanced face recognition using tensor neighborhood preserving discriminant projections

Abstract

We propose in this paper a novel subspace learning method called tensor neighborhood preserving discriminant projections (TNPDP) for face recognition. Compared with the conventional appearance-based face recognition method, the proposed TNPDP does not need to perform image-to-vector conversion and can well preserve the structure of the original image. Different from the existing tensor-based recognition approaches such as tensor subspace analysis (TSA) and discriminant analysis with tensor representation (DATER), TNPDP considers locality and discriminative information simultaneously and can find the optimal tensor subspace that best maintains locality neighborhood manifold and discriminates different classes by maximizing the between-class scatter while minimizing the within-class scatter. Experimental results on two benchmark face databases demonstrate the effectiveness of the proposed method and indicate that TNPDP is better than TSA and DATER, as well as other popular face recognition methods such as principal component analysis (PCA) and linear discrimination analysis (LDA).