Feature selection in the independent component subspace for face recognition

Abstract

This paper addresses the feature selection problem for face recognition in the independent component subspace. While there exists, at least, energy principle to guide the selection of the principle components, the independent components (ICs) are devoid of any energy ranking, and must therefore selected based on their discriminatory power. In addition the independent component features can be selected starting from a much larger pool, or from a combination pool of ICA and PCA features. Four feature selection schemes have been comparatively assessed, and feature subsets are tested on a face database constructed from CMU PIE and FERET databases. The discriminatory features from larger pools are observed to be concentrated around fiduciary spatial details of the nose, the eyes and the facial contour. Overall, face recognition benefits from the feature selection of ICA or PCA components and from the combination of ICA and PCA feature pools.