Adaptive Locality Preserving Projections for Face Recognition

Abstract

This chapter addresses the graph-based linear manifold learning for object recognition. In particular, it introduces an adaptive Locality Preserving Projections (LPP) which has two interesting properties: (i) it does not depend on any parameter, and (ii) there is no correlation between mapped data. The main contribution consists in a parameterless computation of the affinity matrix built on the principle of meaningful and Adaptive neighbors. In addition to the framework of LPP, these two properties have been integrated to the framework of two graph-based embedding techniques: Orthogonal Locality Preserving Projections (OLPP) and Supervised LPP (SLPP). After introducing adaptive affinity matrices and the uncorrelated mapped data constraint, we perform recognition tasks on six public face databases. The results show improvement over those of classic methods such as LPP, OLPP, and SLPP. The proposed method could also be applied to other kinds of objects.

Keywords: Affinity matrix, Classification, Dimensionality reduction, En- hanced Locality Preserving Projections, Face recognition, Graph-based linear embedding, Label information, Laplacian eigenmaps, Laplacian matrix, latent points, Linear discriminant analysis, Locality preserving projections, Nearest neighbor classifier, Orthogonal locality preserving projections, Parameter-less locality preserving projections, Pearson’s coefficient, principal component analysis, Projection directions, Recognition rate, Supervised locality preserving projections.