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Face Recognition Using Local PCA Filters

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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

We propose an efficient feature extraction architecture based on PCANet. Our method performs far better than many traditional artificial feature extraction methods with the help of standalone filter learning and multiscale local feature combination. Such structure cascaded by both linear layers with convolution filters and non-linear layers in binarization process shows better adaptability in different databases. With the help of parallel computing, training time is much shorter than PCANet and also more fixed compared to convolutional neural network. Experiment in LFW and FERET shows that such a data oriented structure shows good performance both on stability and accuracy in various environments.

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Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yida Wang, Shasha Li, Jiani Hu & Weihong Deng

Authors
  1. Yida Wang
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  2. Shasha Li
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  3. Jiani Hu
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  4. Weihong Deng
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Corresponding author

Correspondence to Weihong Deng .

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, Y., Li, S., Hu, J., Deng, W. (2015). Face Recognition Using Local PCA Filters. 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_5

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  • DOI: https://doi.org/10.1007/978-3-319-25417-3_5

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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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