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A New Finger-Vein Recognition Method Based on Hyperspherical Granular Computing

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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

Finger-vein (FV) recognition is an emerging biometric identification technique and has been receiving increasing attention. In this paper, a new finger-vein recognition method is proposed which combines the hyperspherical granular computing with principle component analysis (HSGrC-PCA). We firstly use PCA to obtain the principle components of the FV images. The FV components are then represented as hyperspherical granules. For the training samples, the hyper-spheres corresponding to the classes of training samples can be built using the granular computing classification, thus all of the hyper-spheres form a granule set. For a testing sample, we can classify it into one of the trained hyper-sphere by distance measures. The experimental results show that the proposed method has a good performance in finger-vein recognition efficiency and accuracy.

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Author information

Authors and Affiliations

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

    Zhiyuan Liu, Guimin Jia, Yihua Shi & Jinfeng Yang

Authors
  1. Zhiyuan Liu
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  2. Guimin Jia
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  3. Yihua Shi
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  4. Jinfeng Yang
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Corresponding author

Correspondence to Jinfeng Yang .

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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Liu, Z., Jia, G., Shi, Y., Yang, J. (2015). A New Finger-Vein Recognition Method Based on Hyperspherical Granular Computing. 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_39

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  • DOI: https://doi.org/10.1007/978-3-319-25417-3_39

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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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