Separating geometry from texture to improve face analysis

Abstract

This article studies the effect of preprocessing a classical PCA decomposition using a modified self organizing map (SOM) in order to find shape clusters to improve the texture analysis by means of a pool of PCAs. In most successful view-based recognition systems, shape and texture are jointly used to model statistically a linear or piece-wise linear subspace that optimally explains the face space for a specific database. Our work is aimed at separating the influence that variance in face shape stamps on the set of eigenfaces in the classical PCA decomposition. A set of experiments show the reliability of this new system.