Local Similarity Based Linear Discriminant Analysis for Face Recognition with Single Sample per Person

Abstract

AbstractFisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method coined local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the “divide-conquer” strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. Experimental results on two popular databases show that our method not only generalizes well to SSPP problem but also has strong robustness to expression, illumination, occlusion and time variation.KeywordsFace RecognitionLinear Discriminant AnalysisFace ImageTraining ImageIllumination VariationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.