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

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9010))

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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 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.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Fan Liu, Ye Bi & Zhenmin Tang

  2. Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Yan Cui

Authors
  1. Fan Liu
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  2. Ye Bi
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  3. Yan Cui
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  4. Zhenmin Tang
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Corresponding author

Correspondence to Fan Liu .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C. V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Liu, F., Bi, Y., Cui, Y., Tang, Z. (2015). Local Similarity Based Linear Discriminant Analysis for Face Recognition with Single Sample per Person. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-16634-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16633-9

  • Online ISBN: 978-3-319-16634-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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