A New System of Face Recognition: Using Fuzziness and Sparsity

Abstract

In this article, a new human face recognition scheme is proposed. The proposed system is based on the sparsity and fuzziness, and utilizes independent component analysis (ICA). The scheme includes four parts: a fuzzy comprehensive judgment model for estimating whether the information carried by training samples is enough or not, a proper edge extraction operator to discover more hidden information for single image, ICA feature extractor, and a sparse representation model for correlation coefficient calculation to classify testing samples. In view of the intrinsic patterns of gray information distribution of face images, a weighted fuzzy distance for judgement model and cluster analysis is proposed. The new proposed method is tested on ORL, FERET and UMIST databases. The experiment results demonstrate and illustrate the feasibility of the proposed method and the effective performances on recognition rate.