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Chinese Conference on Biometric Recognition

CCBR 2018: Biometric Recognition pp 29–37Cite as

Finger Vein Recognition Based on Weighted Graph Structural Feature Encoding

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10996))

Abstract

The finger-vein recognition performance is usually sensitive to illumination and pose variation. Exploring suitable feature representation method is therefore significant for finger-vein recognition improvement. In this paper, we propose a novel feature encoding method based on local graph structure (LGS), which behaves better in improving the matching accuracy of features. In terms of the variations of veins in running direction, oriented Gabor filters are firstly used for venous region enhancement. Then, a symmetric cross-weighted local graph structure (SCW-LGS) is proposed to locally represent the gradient relationships among the pixels in a neighborhood of the Gabor enhanced images. Based on SCW-LGS, a multi-orientation feature encoding method is developed for vein network feature representation. Experimental results show that the proposed approach achieves better performance than the state-of-the-art approaches on finger-vein recognition.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61502498, No. 61379102, NO. U1433120) and the Fundamental Research Funds for the Central Universities (NO. 3122017001).

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Authors and Affiliations

  1. Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, China

    Shuyi Li, Haigang Zhang, Guimin Jia & Jinfeng Yang

Authors
  1. Shuyi Li
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  2. Haigang Zhang
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  3. Guimin Jia
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  4. Jinfeng Yang
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Corresponding author

Correspondence to Jinfeng Yang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Xinjiang University, Urumqi, China

    Zhenhong Jia

  5. Tsinghua University, Beijing, China

    Jianjiang Feng

  6. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  7. Xinjiang University, Urumqi, China

    Kurban Ubul

  8. Tsinghua University, Shenzhen, China

    Zhenhua Guo

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Cite this paper

Li, S., Zhang, H., Jia, G., Yang, J. (2018). Finger Vein Recognition Based on Weighted Graph Structural Feature Encoding. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-97909-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97908-3

  • Online ISBN: 978-3-319-97909-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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