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Local Generic Representation for Face Recognition with Single Sample per Person

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

Face recognition with single sample per person (SSPP) is a very challenging task because in such a scenario it is difficult to predict the facial variations of a query sample by the gallery samples. Considering the fact that different parts of human faces have different importance to face recognition, and the fact that the intra-class facial variations can be shared across different subjects, we propose a local generic representation (LGR) based framework for face recognition with SSPP. A local gallery dictionary is built by extracting the neighboring patches from the gallery dataset, while an intra-class variation dictionary is built by using an external generic dataset to predict the possible facial variations (e.g., illuminations, pose, expressions and disguises). LGR minimizes the total representation residual of the query sample over the local gallery dictionary and the generic variation dictionary, and it uses correntropy to measure the representation residual of each patch. Half-quadratic analysis is adopted to solve the optimization problem. LGR takes the advantages of patch based local representation and generic variation representation, showing leading performance in face recognition with SSPP.

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Acknowledgement

This work was (partially) supported by LG Electronics Co., Ltd.

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Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Pengfei Zhu & Lei Zhang

  2. Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Meng Yang

  3. LG Electronics Institute of Technology, Seoul, Korea

    Il-Yong Lee

  4. Department of Computer Science, Yonsei University, Seoul, Korea

    Il-Yong Lee

Authors
  1. Pengfei Zhu
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  2. Meng Yang
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  3. Lei Zhang
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  4. Il-Yong Lee
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Corresponding author

Correspondence to Lei Zhang .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Zhu, P., Yang, M., Zhang, L., Lee, IY. (2015). Local Generic Representation for Face Recognition with Single Sample per Person. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

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