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Learning Discriminant Face Descriptor for Face Recognition

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7725))

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Abstract

Face descriptor is a critical issue for face recognition. Many local face descriptors like Gabor, LBP have exhibited good discriminative ability for face recognition. However, most existing face descriptors are designed in a handcrafted way and the extracted features may not be optimal for face representation and recognition. In this paper, we propose a learning based mechanism to learn the discriminant face descriptor (DFD) optimal for face recognition in a data-driven way. In particular, the discriminant image filters and the optimal weight assignments of neighboring pixels are learned simultaneously to enhance the discriminative ability of the descriptor. In this way, more useful information is extracted and the face recognition performance is improved. Extensive experiments on FERET, CAS-PEAL-R1 and LFW face databases validate the effectiveness and good generalizations of the proposed method.

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Author information

Authors and Affiliations

  1. Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China

    Zhen Lei & Stan Z. Li

Authors
  1. Zhen Lei
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  2. Stan Z. Li
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 151-744, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Lei, Z., Li, S.Z. (2013). Learning Discriminant Face Descriptor for Face Recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_58

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  • DOI: https://doi.org/10.1007/978-3-642-37444-9_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37443-2

  • Online ISBN: 978-3-642-37444-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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