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Collaborative Representation Based Projections for Face Recognition

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Pattern Recognition (CCPR 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 321))

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Abstract

In this paper, we develop a collaborative representation based projections (CRP) for face recognition, which is an unsupervised method. Like SPP and NPE, CRP aims to preserve the sparse reconstruction relations of data. CRP is much faster than SPP since CRP adopts collaborative representation with regularized least square related as objective function while SPP adopts sparse representation related as objective function. Experimental results on ORL and FERET demonstrate that CRP works well in feature extraction and leads to good recognition performance.

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Author information

Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Wankou Yang, Changyin Sun & Qingshan Liu

  2. Face Aging Group, Dept. of Computer Science, UNCW, USA

    Karl Ricanek

Authors
  1. Wankou Yang
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  2. Changyin Sun
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  3. Qingshan Liu
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  4. Karl Ricanek
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Editor information

Editors and Affiliations

  1. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, No.95, Zhongguancun East Road, 100190, Beijing, China

    Cheng-Lin Liu

  2. Department of Automation, Tsinghua University, Haidian District, 100084, Beijing, China

    Changshui Zhang

  3. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, 100190, Beijing, China

    Liang Wang

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© 2012 Springer-Verlag Berlin Heidelberg

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Yang, W., Sun, C., Liu, Q., Ricanek, K. (2012). Collaborative Representation Based Projections for Face Recognition. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_35

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  • DOI: https://doi.org/10.1007/978-3-642-33506-8_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33505-1

  • Online ISBN: 978-3-642-33506-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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