Novel SOM-PCA network for face identification

In this article we present an hybrid SOM+PCA approach for face identification that is based on separating shape and texture information. Shape will be processed by a modified Hausdorff distance SOM and texture processing relies on a modular PCA. In most successfully view-based recognition systems, shape and texture are jointly used to statistically model a linear or piecewise linear subspace that optimally explains the face space for a specific database. Our work is aimed to separate the influence that variance in face shape stamps on the set of eigenfaces in the classical PCA decomposition. In this sense we search for a more efficiently coded face-vector for identification. The ultimate goal consist of finding a non-linear transformation invariant to gesture changes and, in a larger extent, to pose changes. The first part of this paper is dedicated to the shape processor of the system, that is based on a novel shape-based Self Organizing Map, and the second part deals with the subspace PCA decomposition that relies on the SOM clustering. Results are reported by comparing face identification between PCA and the SOM-PCA approach.