Nonnegative-least-square classifier for face recognition

Abstract

In this paper, we propose a novel classification method, based on Nonnegative-Least-Square (NNLS) algorithm, for face recognition. Different from traditional classifiers, in our classifier, we consider each new sample (face) as a nonnegative linear combination of training samples (faces). By forcing the nonnegative constraint on linear coefficients, we obtain the nonnegative sparse representation that automatically discriminates between those classes present in the training set. Experimental results show the promising aspects of new classifier when comparing with the most popular classifiers such as Nearest Neighborhood (NN), Nearest Centroid (NC), and Nearest Subspace (NS) in terms of recognition accuracy, efficiency, and numerical stability. Eigenfaces, Fisherfaces, and Laplacianfaces are performed on Yale and ORL databases as feature extraction in these experiments. © 2009 Springer Berlin Heidelberg.