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Capacity Analysis of Hand-Dorsa Vein Features Based on Image Coding

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Biometric Recognition (CCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8833))

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Abstract

This paper presents a method to calculate capacity of hand vein features by coding divided sub-images. An image coding model is proposed in this paper, including gray level inertia moment extraction and feature coding. The proposed method is tested on a database of 1000 images from 100 individuals built up by a custom-made acquisition device. The experiment results indicate that the capacity of hand vein features supports over 100 thousand individuals.

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References

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Author information

Authors and Affiliations

  1. College of Information Engineering, North China University of Technology, Beijing, 100144, China

    Yiding Wang & Xi Cao

Authors
  1. Yiding Wang
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  2. Xi Cao
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Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, 100190, Beijing, China

    Zhenan Sun

  2. Institute of Computing Technology, Chinese Academy of Sciences, No.6 Kexueyuan South Road, P.O. Box 2704, 100190, HaiDian, Beijing, China

    Shiguang Shan

  3. School of Information Science and Engineering, Shenyang University of Technology, No.111, Shenliao West Road, Economic and Technological Development Zone, 110870, Shenyang, China

    Haifeng Sang  & Weiqi Yuan  & 

  4. Software Engineering Institute, Tsinghua University, No. 1 Tsinghua Yuan, Haidian District, 100084, Beijing, China

    Jie Zhou

  5. School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, 100191, Beijing, China

    Yunhong Wang

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Wang, Y., Cao, X. (2014). Capacity Analysis of Hand-Dorsa Vein Features Based on Image Coding. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-12484-1_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12483-4

  • Online ISBN: 978-3-319-12484-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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