Selective locality preserving projections for face recognition

Abstract

Recently, a graph-based method was proposed for Linear Dimensionality Reduction (LDR). It is based on Locality Preserving Projections (LPP). LPP is a typical linear graph-based dimensionality reduction (DR) method that has been successfully applied in many practical problems such as face recognition. LPP is essentially a linearized version of Laplacian Eigenmaps. When dealing with face recognition problems, LPP is preceded by a Principal Component Analysis (PCA) step in order to avoid possible singularities. Both PCA and LPP are computed by solving an eigen decomposition problem. In this paper, we propose a novel approach called "Selective Locality Preserving Projections" that performs an eigenvector selection associated with LPP. Consequently, the problem of dimension estimation for LPP is solved. Moreover, we propose a selective approach that performs eigenvector selection for the case where the mapped samples are formed by concatenating the output of PCA and LPP. We have tested our proposed approaches on several public face data sets. Experiments on ORL, UMIST, and YALE Face Databases show significant performance improvements in recognition over the classical LPP. The proposed approach lends itself nicely to many biometric applications.