Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
Source code and results: https://pypi.org/project/bob.hobpad2.veins.
- 6.
- 7.
- 8.
References
Jain AK, Flynn P, Ross AA (eds) (2008) Handbook of biometrics. Springer, Berlin. https://doi.org/10.1007/978-0-387-71041-9
Finger vein authentication: white paper. Technical report, Hitachi, Ltd (2006)
Kono M, Ueki H, Umemura SI (2002) Near-infrared finger vein patterns for personal identification. Appl Opt 41(35):7429–7436. https://doi.org/10.1364/AO.41.007429
Kono M, Umemura S, Miyatake T, Harada K, Ito Y, Ueki H (2004) Personal identification system. US Patent 6,813,010. https://www.google.com/patents/US6813010
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 230
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. https://doi.org/10.1109/ICB.2015.7139056. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7139056
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
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 group
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 181–190
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
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. https://doi.org/10.1109/SITIS.2015.74
Zhou Y, Kumar A (2011) Human identification using palm-vein images. IEEE Trans Inf Forensics Secur 6(4):1259–1274
Kang W, Wu Q (2014) Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Trans Inf Forensics Secur 9(11):1974–1985
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 1–4
Mirmohamadsadeghi L, Drygajlo A (2014) Palm vein recognition with local texture patterns. IET Biom 1–9
Swain M, Ballard D (1991) Color indexing. Int J Comput Vis 7(1):11–32
Ton B (2012) Vascular pattern of the finger: biometric of the future? Sensor design, data collection and performance verification. Master’s thesis, University of Twente
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 1–5
Xi X, Yang G, Yin Y, Meng X (2013) Finger vein recognition with personalized feature selection. Sensors 13(9):11243–11259
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. https://doi.org/10.1109/BTAS.2014.6996225
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. https://doi.org/10.1109/WIFS.2015.7368599
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. https://doi.org/10.1109/CIS.2009.144
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. https://doi.org/10.1109/BTAS.2015.7358762
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 1269–1272
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):194–203
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
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Anjos, A., Tome, P., Marcel, S. (2019). An Introduction to Vein Presentation Attacks and Detection. In: Marcel, S., Nixon, M., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-92627-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-92627-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92626-1
Online ISBN: 978-3-319-92627-8
eBook Packages: Computer ScienceComputer Science (R0)