• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Local Similarity based Discriminant Analysis for Face Recognition

Vol. 9, No. 11, November 29, 2015
10.3837/tiis.2015.11.014, Download Paper (Free):

Abstract

Fisher 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 called 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. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.


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Cite this article

[IEEE Style]
X. Xiang, F. Liu, Y. Bi, Y. Wang and J. Tang, "Local Similarity based Discriminant Analysis for Face Recognition," KSII Transactions on Internet and Information Systems, vol. 9, no. 11, pp. 4502-4518, 2015. DOI: 10.3837/tiis.2015.11.014.

[ACM Style]
Xinguang Xiang, Fan Liu, Ye Bi, Yanfang Wang, and Jinhui Tang. 2015. Local Similarity based Discriminant Analysis for Face Recognition. KSII Transactions on Internet and Information Systems, 9, 11, (2015), 4502-4518. DOI: 10.3837/tiis.2015.11.014.