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Robust Recognition of Noisy and Partially Occluded Faces Using Iteratively Reweighted Fitting of Eigenfaces

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 4261))

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Abstract

Robust recognition of noisy and partially occluded faces is essential for an automated face recognition system, but most appearance-based methods (e.g., Eigenfaces) are sensitive to these factors. In this paper, we propose to address this problem using an iteratively reweighted fitting of the Eigenfaces method (IRF-Eigenfaces). Unlike Eigenfaces fitting, in which a simple linear projection operation is used to extract the feature vector, the IRF-Eigenfaces method first defines a generalized objective function and then uses the iteratively reweighted least-squares (IRLS) fitting algorithm to extract the feature vector by minimizing the generalized objective function. Our simulated and experimental results on the AR database show that IRF-Eigenfaces is far superior to both Eigenfaces and to the local probabilistic method in recognizing noisy and partially occluded faces.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Wangmeng Zuo & Kuanquan Wang

  2. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    David Zhang

Authors
  1. Wangmeng Zuo

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  2. Kuanquan Wang

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  3. David Zhang

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Editor information

Editors and Affiliations

  1. College of Computer Science, Zhejiang University, China

    Yueting Zhuang

  2. Department of Computer Science and Technology, Tsinghua University, P.R. China

    Shi-Qiang Yang

  3. Microsoft Corporation, Microsoft China R&D Group, 49 Zhichun Road, 100080, Beijing, China

    Yong Rui

  4. College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, Zhejiang Province, China

    Qinming He

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© 2006 Springer-Verlag Berlin Heidelberg

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Zuo, W., Wang, K., Zhang, D. (2006). Robust Recognition of Noisy and Partially Occluded Faces Using Iteratively Reweighted Fitting of Eigenfaces. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_96

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