A new face recognition method based on image decomposition for single sample per person problem

Abstract

The image decomposition based method is one of the efficient and important face recognition solutions for the single sample per person problem. The low image decomposition performance and the unconvincing reconstruction of the approximation image are the two main limitations of the previous methods. In this paper, a new single sample face recognition method based on lower-upper (LU) decomposition algorithm is proposed. The procedure of the proposed method is as following. First, the single sample and its transpose are decomposed to two sets of basis images by using the LU decomposition algorithm, which is more efficient than the image decomposition algorithms of the previous works. Two approximation images are reconstructed from the two basis image sets by the reverse thinking approach based on experimental analysis. Then, the fisher linear discriminant analysis (FLDA) algorithm is used to evaluate the optimal projection space by using the new training set consisting of the single sample and its two approximation images for each person. Finally, the nearest neighbor classifier based on Euclidean distance is adopted as the final classification. We make two main contributions: one is that we propose to decompose the single sample and its transpose using the efficient LU decomposition algorithm, and reorder each basis image set according to the basis image energy; the other is that we present a reverse thinking approach based on experimental analysis to reconstruct the approximation image. The performance of the proposed method is verified using four public face databases, namely FERET, AR, ORL and Yale B. The experimental results indicate that the proposed method is efficient and outperforms several state-of-the-art approaches which are proposed to address the single sample per person problem.