An Introduction to Vein Presentation Attacks and Detection

  • André AnjosEmail author
  • Pedro Tome
  • Sébastien Marcel
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The domain of presentation attacks (PAs), including vulnerability studies and detection (PAD), remains very much unexplored by available scientific literature in biometric vein recognition. Contrary to other modalities that use visual spectral sensors for capturing biometric samples, vein biometrics is typically implemented with near-infrared imaging. The use of invisible light spectra challenges the creation of instruments, but does not render it impossible. In this chapter, we provide an overview of current landscape for PA manufacturing in possible attack vectors for vein recognition, describe existing public databases and baseline techniques to counter such attacks. The reader will also find material to reproduce experiments and findings for finger vein recognition systems. We provide this material with the hope that it will be extended to other vein recognition systems and improved in time.



The authors would like to thank the Swiss Centre for Biometrics Research and Testing and the Swiss Commission for Technology and Innovation (CTI) for supporting the research leading to some of results published in this book chapter.


  1. 1.
    Jain AK, Flynn P, Ross AA (eds) (2008) Handbook of biometrics. Springer, Berlin. Scholar
  2. 2.
    Finger vein authentication: white paper. Technical report, Hitachi, Ltd (2006)Google Scholar
  3. 3.
    Kono M, Ueki H, Umemura SI (2002) Near-infrared finger vein patterns for personal identification. Appl Opt 41(35):7429–7436. Scholar
  4. 4.
    Kono M, Umemura S, Miyatake T, Harada K, Ito Y, Ueki H (2004) Personal identification system. US Patent 6,813,010.
  5. 5.
    Tome P, Vanoni M, Marcel S (2014) On the vulnerability of finger vein recognition to spoofing. In: IEEE international conference of the biometrics special interest group (BIOSIG), vol 230Google Scholar
  6. 6.
    Tome P, Marcel S (2015) On the vulnerability of palm vein recognition to spoofing attacks. In: The 8th IAPR international conference on biometrics (ICB), pp 319–325.
  7. 7.
    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.
  8. 8.
    Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing. In: Proceedings of the 11th international conference of the biometrics special interest groupGoogle Scholar
  9. 9.
    Ruiz-Albacete V, Tome-Gonzalez P, Alonso-Fernandez F, Galbally J, Fierrez J, Ortega-Garcia J (2008) Direct attacks using fake images in iris verification. In: Proceedings of the COST 2101 workshop on biometrics and identity management, BIOID. LNCS, vol 5372. Springer, Berlin, pp 181–190Google Scholar
  10. 10.
    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. Scholar
  11. 11.
    Raghavendra R, Busch C (2015) Presentation attack detection algorithms for finger vein biometrics: a comprehensive study. In: 2015 11th international conference on signal-image technology internet-based systems (SITIS), pp 628–632.
  12. 12.
    Zhou Y, Kumar A (2011) Human identification using palm-vein images. IEEE Trans Inf Forensics Secur 6(4):1259–1274CrossRefGoogle Scholar
  13. 13.
    Kang W, Wu Q (2014) Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Trans Inf Forensics Secur 9(11):1974–1985CrossRefGoogle Scholar
  14. 14.
    Zhang J, Yang J (2009) Finger-vein image enhancement based on combination of gray-level grouping and circular Gabor filter. In: International conference on information engineering and computer science (ICIECS), pp 1–4Google Scholar
  15. 15.
    Mirmohamadsadeghi L, Drygajlo A (2014) Palm vein recognition with local texture patterns. IET Biom 1–9Google Scholar
  16. 16.
    Swain M, Ballard D (1991) Color indexing. Int J Comput Vis 7(1):11–32CrossRefGoogle Scholar
  17. 17.
    Ton B (2012) Vascular pattern of the finger: biometric of the future? Sensor design, data collection and performance verification. Master’s thesis, University of TwenteGoogle Scholar
  18. 18.
    Ton B, Veldhuis R (2013) A high quality finger vascular pattern dataset collected using a custom designed capturing device. In: IEEE international conference on biometrics (ICB), pp 1–5Google Scholar
  19. 19.
    Xi X, Yang G, Yin Y, Meng X (2013) Finger vein recognition with personalized feature selection. Sensors 13(9):11243–11259CrossRefGoogle Scholar
  20. 20.
    Raghavendra R, Raja KB, Surbiryala J, Busch C (2014) A low-cost multimodal biometric sensor to capture finger vein and fingerprint. In: IEEE international joint conference on biometrics, pp 1–7.
  21. 21.
    Tirunagari S, Poh N, Bober M, Windridge D (2015) Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics. In: 2015 IEEE international workshop on information forensics and security (WIFS), pp 1–6.
  22. 22.
    Qin B, Pan J-F, Cao G-Z, Du G-G (2009) The anti-spoofing study of vein identification system. In: 2009 international conference on computational intelligence and security, vol 2, pp 357–360.
  23. 23.
    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.
  24. 24.
    Huang B, Dai Y, Li R, Tang D, Li W (2010) Finger-vein authentication based on wide line detector and pattern normalization. In: International conference on pattern recognition (ICPR), pp 1269–1272Google Scholar
  25. 25.
    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(4):194–203CrossRefGoogle Scholar
  26. 26.
    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):1185–1194CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.Universidad Autonoma de MadridMadridSpain

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