Skip to main content
SpringerLink
Log in

Extensive Threat Analysis of Vein Attack Databases and Attack Detection by Fusion of Comparison Scores

  • Chapter
  • First Online:
Handbook of Biometric Anti-Spoofing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The last decade has brought forward many great contributions regarding presentation attack detection for the domain of finger and hand vein biometrics. Among those contributions, one is able to find a variety of different attack databases that are either private or made publicly available to the research community. However, it is not always shown whether the used attack samples hold the capability to actually deceive a realistic vein recognition system. Inspired by previous works, this study provides a systematic threat evaluation including three publicly available finger vein attack databases and one private dorsal hand vein database. To do so, 14 distinct vein recognition schemes are confronted with attack samples, and the percentage of wrongly accepted attack samples is then reported as the Impostor Attack Presentation Match Rate. As a second step, comparison scores from different recognition schemes are combined using score level fusion with the goal of performing presentation attack detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://wavelab.at/sources/PLUS-FV3-PALMAR-Image-Spoof/.

  2. 2.

    https://www.idiap.ch/en/dataset/vera-fingervein.

  3. 3.

    https://github.com/BIP-Lab/SCUT-SFVD.

  4. 4.

    https://www.neurotechnology.com/verifinger.html.

References

  1. ISO/IEC JTC 1/SC 37 Biometrics (2017) Information technology — biometric presentation attack detection — part 3: testing and reporting. Standard ISO/IEC 30107-3:2017, International Organization for Standardization, Geneva, CH. https://www.iso.org/standard/67381.html

  2. Krissler J, Julian (2018) Venenerkennung hacken - Vom Fall der letzten Bastion biometrischer Systeme. Chaos Computer Club e.V. https://doi.org/10.5446/39201. Accessed 21 Jan 2021

  3. Kolberg J, Gomez-Barrero M, Venkatesh S, Ramachandra R, Busch C (2020) Presentation attack detection for finger recognition. Springer International Publishing, Cham, pp 435–463. https://doi.org/10.1007/978-3-030-27731-4_14

  4. Nguyen DT, Park YH, Shin KY, Kwon SY, Lee HC, Park KR (2013) Fake finger-vein image detection based on fourier and wavelet transforms. Digit Signal Process 23(5):1401–1413. https://doi.org/10.1016/j.dsp.2013.04.001. www.sciencedirect.com/science/article/pii/S1051200413000808

  5. Tome P, Vanoni M, Marcel S (2014) On the vulnerability of finger vein recognition to spoofing. In: 2014 international conference of the biometrics special interest group (BIOSIG), pp 1–10

    Google Scholar 

  6. Tome P, Raghavendra R, Busch C, Tirunagari S, Poh N, Shekar BH, Gragnaniello D, Sansone C, Verdoliva L, Marcel S (2015) The 1st competition on counter measures to finger vein spoofing attacks. In: 2015 international conference on biometrics (ICB), pp 513–518. https://doi.org/10.1109/ICB.2015.7139067

  7. Qiu X, Kang W, Tian S, Jia W, Huang Z (2018) Finger vein presentation attack detection using total variation decomposition. IEEE Trans Inf Forensics Secur 13(2):465–477. https://doi.org/10.1109/TIFS.2017.2756598

    Article  Google Scholar 

  8. Patil I, Bhilare S, Kanhangad V (2016) Assessing vulnerability of dorsal hand-vein verification system to spoofing attacks using smartphone camera. In: 2016 IEEE international conference on identity, security and behavior analysis (ISBA), pp 1–6

    Google Scholar 

  9. Otsuka A, Ohki T, Morita R, Inuma M, Imai H (2016) Security evaluation of a finger vein authentication algorithm against wolf attack. In: 37th IEEE symposium on security and privacy, San Jose, CA

    Google Scholar 

  10. Debiasi L, Kauba C, Hofbauer H, Prommegger B, Uhl A (2020) Presentation attacks and detection in finger- and hand-vein recognition. In: Proceedings of the joint Austrian computer vision and robotics workshop (ACVRW’20), Graz, Austria, pp 65–70. https://doi.org/10.3217/978-3-85125-752-6-16

  11. Schuiki J, Prommegger B, Uhl A (2021) Confronting a variety of finger vein recognition algorithms with wax presentation attack artefacts. In: Proceedings of the 9th IEEE international workshop on biometrics and forensics (IWBF’21), Rome, Italy (moved to virtual), pp 1–6

    Google Scholar 

  12. Schuiki J, Wimmer G, Uhl A (2021) Vulnerability assessment and presentation attack detection using a set of distinct finger vein recognition algorithms. In: 2021 IEEE international joint conference on biometrics (IJCB), Shenzhen (China), pp 1–7. https://doi.org/10.1109/IJCB52358.2021.9484351

  13. Qiu X, Tian S, Kang W, Jia W, Wu Q (2017) Finger vein presentation attack detection using convolutional neural networks. In: Zhou J, Wang Y, Sun Z, Xu Y, Shen L, Feng J, Shan S, Qiao Y, Guo Z, Yu S (eds) Biometric recognition. Springer International Publishing, Cham, pp 296–305

    Chapter  Google Scholar 

  14. Herzog T, Uhl A (2020) Analysing a vein liveness detection scheme. In: Proceedings of the 8th international workshop on biometrics and forensics (IWBF’20), Porto, Portugal, pp 1–6

    Google Scholar 

  15. Schuiki J, Uhl A (2020) Improved liveness detection in dorsal hand vein videos using photoplethysmography. In: Proceedings of the IEEE 19th international conference of the biometrics special interest group (BIOSIG 2020). Darmstadt, Germany, pp 57–65

    Google Scholar 

  16. Kauba C, Prommegger B, Uhl A (2018) Focussing the beam - a new laser illumination based data set providing insights to finger-vein recognition. In: 2018 IEEE 9th international conference on biometrics theory, applications and systems (BTAS), Los Angeles, California, USA, pp 1–9. https://doi.org/10.1109/BTAS.2018.8698588

  17. Choi JH, Song W, Kim T, Lee SR, Kim HC (2009) Finger vein extraction using gradient normalization and principal curvature. In: Image processing: machine vision applications II, vol 7251, pp 7251–7251–9. https://doi.org/10.1117/12.810458

  18. Raghavendra R, Avinash M, Marcel S, Busch C (2015) Finger vein liveness detection using motion magnification. In: 2015 IEEE 7th international conference on biometrics theory, applications and systems (BTAS), pp 1–7

    Google Scholar 

  19. Miura N, Nagasaka A, Miyatake T (2007) Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE - Trans Inf Syst E90-D(8):1185–1194

    Google Scholar 

  20. Miura N, Nagasaka A, Miyatake T (2004) Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach Vis Appl 15:194–203. https://doi.org/10.1007/s00138-004-0149-2

    Article  Google Scholar 

  21. Huang B, Dai Y, Li R, Tang D, Li W (2010) Finger-vein authentication based on wide line detector and pattern normalization. In: 2010 20th international conference on pattern recognition, pp 1269–1272. https://doi.org/10.1109/ICPR.2010.316

  22. Kumar A, Zhou Y (2012) Human identification using finger images. IEEE Trans Image Process 21(4):2228–2244. https://doi.org/10.1109/TIP.2011.2171697

    Article  MathSciNet  MATH  Google Scholar 

  23. Starck J, Fadili J, Murtagh F (2007) The undecimated wavelet decomposition and its reconstruction. IEEE Trans Image Process 16(2):297–309. https://doi.org/10.1109/TIP.2006.887733

    Article  MathSciNet  Google Scholar 

  24. Yang L, Yang G, Yin Y, Xi X (2018) Finger vein recognition with anatomy structure analysis. IEEE Trans Circuits Syst Video Technol 28(8):1892–1905. https://doi.org/10.1109/TCSVT.2017.2684833

    Article  Google Scholar 

  25. Matsuda Y, Miura N, Nagasaka A, Kiyomizu H, Miyatake T (2016) Finger-vein authentication based on deformation-tolerant feature-point matching. Mach Vis Appl 27. https://doi.org/10.1007/s00138-015-0745-3

  26. Kauba C, Reissig J, Uhl A (2014) Pre-processing cascades and fusion in finger vein recognition. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’14), Darmstadt, Germany

    Google Scholar 

  27. Stockman G, Shapiro LG (2001) Computer vision, 1st edn. Prentice Hall PTR, USA

    Google Scholar 

  28. Lee EC, Lee HC, Park KR (2009) Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction. Int J Imaging Syst Technol 19(3):179–186

    Article  Google Scholar 

  29. Wimmer G, Prommegger B, Uhl A (2020) Finger vein recognition and intra-subject similarity evaluation of finger veins using the cnn triplet loss. In: Proceedings of the 25th international conference on pattern recognition (ICPR), pp 400–406

    Google Scholar 

  30. Linortner M, Uhl A (2021) Towards match-on-card finger vein recognition. In: Proceedings of the 2021 ACM workshop on information hiding and multimedia security, IH &MMSec ’21. Association for Computing Machinery, New York, NY, USA, pp 87–92. https://doi.org/10.1145/3437880.3460406

  31. Xu H, Veldhuis RNJ, Bazen AM, Kevenaar TAM, Akkermans TAHM, Gokberk B (2009) Fingerprint verification using spectral minutiae representations. IEEE Trans Inf Forensics Secur 4(3):397–409. https://doi.org/10.1109/TIFS.2009.2021692

    Article  Google Scholar 

  32. Chingovska I, Mohammadi A, Anjos A, Marcel S (2019) Evaluation methodologies for biometric presentation attack detection. In: Marcel S, Nixon MS, Fierrez J, Evans N (eds) Handbook of biometric anti-spoofing: presentation attack detection. Springer International Publishing, Cham, pp 457–480. https://doi.org/10.1007/978-3-319-92627-8_20

  33. Linortner M, Uhl A (2021) Veinplus+: a publicly available and free software framework for vein recognition. In: 2021 international conference of the biometrics special interest group (BIOSIG), pp 1–5. https://doi.org/10.1109/BIOSIG52210.2021.9548286

  34. Kauba C, Prommegger B, Uhl A (2019) Openvein - an open-source modular multipurpose finger vein scanner design. In: Uhl A, Busch C, Marcel S, Veldhuis R (eds) Handbook of vascular biometrics, Chap 3. Springer Nature Switzerland AG, Cham, Switzerland, pp 77–111. https://doi.org/10.1007/978-3-030-27731-4_3

  35. Sequeira AF, Ferryman J, Chen L, Galdi C, Dugelay JL, Chiesa V, Uhl A, Prommegger B, Kauba C, Kirchgasser S, Grudzien A, Kowalski M, Szklarski L, Maik P, Gmitrowicz P (2018) Protect multimodal db: a multimodal biometrics dataset envisaging border control. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG’18), Darmstadt, Germany, pp 1–8. https://doi.org/10.23919/BIOSIG.2018.8552926

  36. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2004) Fvc 2004: third fingerprint verification competition. In: Zhang D, Jain AK (eds) Biometric authentication. Springer, Berlin, pp 1–7

    Google Scholar 

  37. Snelick R, Indovina M, Yen J, Mink A (2003) Multimodal biometrics: issues in design and testing. In: Proceedings of the 5th international conference on multimodal interfaces, ICMI ’03. Association for Computing Machinery, New York, NY, USA, pp 68–72. https://doi.org/10.1145/958432.958447

  38. Snelick R, Uludag U, Mink A, Indovina M, Jain A (2005) Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans Pattern Anal Mach Intell 27(3):450–455. https://doi.org/10.1109/TPAMI.2005.57

    Article  Google Scholar 

  39. Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recognit 38(12):2270–2285. https://doi.org/10.1016/j.patcog.2005.01.012. www.sciencedirect.com/science/article/pii/S0031320305000592

  40. He M, Horng SJ, Fan P, Run RS, Chen RJ, Lai JL, Khan MK, Sentosa KO (2010) Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recognit 43(5):1789–1800. https://doi.org/10.1016/j.patcog.2009.11.018. www.sciencedirect.com/science/article/pii/S0031320309004373

Download references

Acknowledgements

This work has been partially supported by the Austrian Science Fund and the Salzburg State Government, FWF project no. P32201.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Salzburg, Salzburg, Austria

    Johannes Schuiki, Michael Linortner, Georg Wimmer & Andreas Uhl

Authors
  1. Johannes Schuiki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Linortner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Georg Wimmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Uhl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Johannes Schuiki , Michael Linortner , Georg Wimmer or Andreas Uhl .

Editor information

Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. Universidad Autonoma de Madrid, Madrid, Spain

    Julian Fierrez

  3. EURECOM, Biot Sophia Antipolis, France

    Nicholas Evans

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Schuiki, J., Linortner, M., Wimmer, G., Uhl, A. (2023). Extensive Threat Analysis of Vein Attack Databases and Attack Detection by Fusion of Comparison Scores. In: Marcel, S., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Singapore. https://doi.org/10.1007/978-981-19-5288-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5288-3_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5287-6

  • Online ISBN: 978-981-19-5288-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation