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International Conference on Advances in Cyber Security

ACeS 2020: Advances in Cyber Security pp 468–478Cite as

Finger Vein Presentation Attack Detection with Optimized LBP Variants

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1347))

Abstract

Finger vein-based authentication systems have been proven to be promisingly accurate in identifying a person. However, the system is still highly vulnerable from presentation attack. Presentation attack is one of the most commonly found attacks in typical biometrics systems. A printed finger vein image could be used to bypass the system with ease. Various presentation attack detection methods based on texture and liveness analysis have been presented to encounter such issue. In this paper, our aim is to apply hyper-parameters tuning on Local Binary Pattern to gain the best features set for presentation attack detection in finger vein recognition. Using an automated hyper-parameter tuning approach, we find a set of optimized parameters which are able to extract the best features for presentation attack detection. Experiment results demonstrate that the proposed method is able to yield a significant high accuracy in distinguishing genuine images from fake images.

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References

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Acknowledgements

Our thanks to BIP Lab South China University of Technology for allowing us to use the SCUT-FVD Finger Vein Database they had collected. This work is supported by Fundamental Research Grant Scheme (FRGS) of Ministry of Higher Education Malaysia (FRGS Grant No: MMUE/190047).

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia

    W. Q. Janie Lee, Thian Song Ong, Tee Connie & H. T. Jackson

Authors
  1. W. Q. Janie Lee
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  2. Thian Song Ong
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  3. Tee Connie
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  4. H. T. Jackson
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Corresponding author

Correspondence to W. Q. Janie Lee .

Editor information

Editors and Affiliations

  1. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

  2. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  3. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

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Cite this paper

Lee, W.Q.J., Ong, T.S., Connie, T., Jackson, H.T. (2021). Finger Vein Presentation Attack Detection with Optimized LBP Variants. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_31

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  • DOI: https://doi.org/10.1007/978-981-33-6835-4_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6834-7

  • Online ISBN: 978-981-33-6835-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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