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Face Recognition via Compact Fisher Vector

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Biometric Recognition (CCBR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9428))

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Abstract

Efficient encoding of facial descriptors remains to be a major topic for face recognition. Among various methods, Fisher vector (FV) representations have shown satisfying performance on most benchmark datasets. However, its representation is huge. In this paper, we present a novel approach to make Fisher vector compact and improves its performance. We utilize handcrafted low-level descriptors as FV do. However, we retain only 1st order statistics of FV, introduce Gaussian block to sparsify FV, alter its formulation, and normalize properly. We evaluate our method on LFW and FERET dataset, and result shows our method effectively compresses Fisher vector and achieves satisfying result at the same time.

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Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunication, No 10, Xitucheng Road, Haidian District, Beijing, People’s Republic of China

    Hongjun Wang & Weihong Deng

Authors
  1. Hongjun Wang
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  2. Weihong Deng
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Corresponding author

Correspondence to Hongjun Wang .

Editor information

Editors and Affiliations

  1. Civil Aviation University of China, Tianjin, China

    Jinfeng Yang

  2. and Technology, Tianjin Institute of Science, Tianjin, China

    Jucheng Yang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  5. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

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© 2015 Springer International Publishing Switzerland

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Wang, H., Deng, W. (2015). Face Recognition via Compact Fisher Vector. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-25417-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25416-6

  • Online ISBN: 978-3-319-25417-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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