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Study of Heterogeneous Dorsal Hand Vein Recognition Based on Multi-device

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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

The effectiveness of dorsal hand vein recognition technology depends on image quality. The problem of dorsal hand vein image heterogeneity is becoming increasingly prominent in the era of big data. In the multi-device acquisition process, image ROI size, contrast, sharpness, position shift and image rotation are the main parameters of image heterogeneity. In order to explore the effects of different parameters on the image multi-device recognition, we adjusted 5 quality parameters of dorsal hand vein image individually first of all. Then, we used different recognition algorithms for experiment, and quantitatively analyzed the effect of different parameters on the heterogeneous dorsal hand vein image recognition by the improvement of recognition rate. Finally, the method of multi-parameter adjustment was proposed to improve recognition rate.

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References

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Author information

Authors and Affiliations

  1. College of Electronic Information Engineering, North China University of Technology, Beijing, China

    Yiding Wang & Linlin Xu

Authors
  1. Yiding Wang
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  2. Linlin Xu
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Corresponding author

Correspondence to Linlin Xu .

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., Xu, L. (2015). Study of Heterogeneous Dorsal Hand Vein Recognition Based on Multi-device. 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_34

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  • DOI: https://doi.org/10.1007/978-3-319-25417-3_34

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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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