Face recognition using discriminative locality preserving vectors

Abstract

We proposed an effective face recognition method based on the discriminative locality preserving vectors method (DLPV). Using the analysis of eigenspectrum modeling of locality preserving projections, we selected the reliable face variation subspace of LPP to construct the locality preserving vectors to characterize the data set. The discriminative locality preserving vectors (DLPV) method is based on the discriminant analysis on the locality preserving vectors. Furthermore, the theoretical analysis showed that the DLPV is viewed as a generalized discriminative common vector, null space linear discriminant analysis and null space discriminant locality preserving projections, which gave the intuitive motivation of our method. Extensive experimental results obtained on four well-known face databases (ORL, Yale, Extended Yale B and CMU PIE) demonstrated the effectiveness of the proposed DLPV method.