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Chinese Conference on Biometric Recognition

CCBR 2017: Biometric Recognition pp 370–379Cite as

Weighted Graph Based Description for Finger-Vein Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

Abstract

The randomness of vein networks determines the discrimination of finger veins patterns in recognition. Effectively describing the random patterns is therefore very important for finger-vein based biometrics. In this paper, a new graph-based method is proposed for finger-vein network feature representation. A block-wise action is first done for graph node generation from a finger-vein image. By applying Delaunay triangulation to these obtained nodes, the graph edges are then built for featuring the spatial relations between images blocks. For a given feature space, each of these edges can locally represent a relationship between two adjacent nodes. Considering local variations in image contents, the graph edges are further weighted node-wisely using the statistics of image blocks. Thus, a graph can globally represent a finger-vein network, and its weighted edges can locally describe the relations of image blocks. Experimental results on two image databases totally 1,200 image samples show that the proposed method performs well in finger-vein recognition.

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Acknowledgements

This work is jointly supported by National Natural Science Foundation of China (Nos. 61379102, U1433120, 61502498) and the Fundamental Research Funds for the Central Universities (No. 3122014C003).

Author information

Authors and Affiliations

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

    Ziyun Ye & Jinfeng Yang

  2. Advanced Technologies Applications Center, Havana, Cuba

    Jose Hernandez Palancar

Authors
  1. Ziyun Ye
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  2. Jinfeng Yang
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  3. Jose Hernandez Palancar
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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. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

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Cite this paper

Ye, Z., Yang, J., Palancar, J.H. (2017). Weighted Graph Based Description for Finger-Vein Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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