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Robust Face Recognition Under Varying Illumination and Occlusion via Single Layer Networks

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Biometric Recognition (CCBR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9967))

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Abstract

Feature extraction plays a significant role in face recognition, it is desired to extract robust feature to eliminate the effect of variations caused by illumination and occlusion. Motivated by convolutional architecture of deep learning and the advantages of KMeans algorithm in filters learning. In this paper, a simple yet effective face recognition approach is proposed, which consists of three components: convolutional filters learning, nonlinear transformation and feature pooling. Concretely, firstly, KMeans is employed to construct the convolutional filters quickly on preprocessed image patches. Secondly, hyperbolic tangent is applied for nonlinear transformation on the convoluted images. Thirdly, multi levels of spatial pyramid pooling is utilized to incorporate spatial geometry information of learned features. Recognition phase only requires an efficient linear regression classifier. Experimental results on two representative databases AR and ExtendedYaleB demonstrate strong robustness of our method against real disguise, illumination, block occlusion, as well as pixel corruption.

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Author information

Authors and Affiliations

  1. College of Mathematics and Statistics, Chongqing University, Chongqing, People’s Republic of China

    Shu Feng

Authors
  1. Shu Feng
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Corresponding author

Correspondence to Shu Feng .

Editor information

Editors and Affiliations

  1. Sichuan University , Chengdu, China

    Zhisheng You

  2. Tsinghua University , Beijing, China

    Jie Zhou

  3. Beihang University , Beijing, China

    Yunhong Wang

  4. Chinese Academy of Sciences , Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences , Beijing, China

    Shiguang Shan

  6. Sun Yat-sen University , Guangzhou, China

    Weishi Zheng

  7. Tsinghua University , Beijing, China

    Jianjiang Feng

  8. Sichuan University , Chengdu, China

    Qijun Zhao

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Feng, S. (2016). Robust Face Recognition Under Varying Illumination and Occlusion via Single Layer Networks. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-46654-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46653-8

  • Online ISBN: 978-3-319-46654-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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