Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition

A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, the neighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.