Large Scale Experiments on Fingerprint Liveness Detection

  • Gian Luca Marcialis
  • Luca Ghiani
  • Katja Vetter
  • Dirk Morgeneier
  • Fabio Roli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


Fingerprint liveness detection consists in extracting measurements, from a fingerprint image, allowing to distinguish between an “alive” fingerprint image, that is, an image coming from the fingertip of the claimed identity, and an artificial replica. Several algorithms have been proposed so far, but the robustness of their performance has not yet been compared when varying several environmental conditions. In this paper, we present a set of experiments investigating the performance of several feature sets designed for fingerprint liveness detection. In particular we assessed the decrease of performance when varying the pressure and the environmental illumination as well as the size of the region of interest (ROI) used for extracting such features. Experimental results on a large data set show the different dependence of some features sets on the investigated conditions.


Local Binary Pattern Multi Layer Perceptron Large Scale Experiment Baseline System Fingerprint Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Jain, A.K., Flynn, P., Ross, A.: Handbook of Biometrics. Springer (2007) ISBN 9780387710402Google Scholar
  2. 2.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003) ISBN 0387954317zbMATHGoogle Scholar
  3. 3.
    Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, H.: Impact of artificial ‘gummy’ fingers on fingerprint systems. In: Proceedings of SPIE, vol. 4677 (2002)Google Scholar
  4. 4.
    Coli, P., Marcialis, G.L., Roli, F.: Vitality Detection from Fingerprint Images: A Critical Survey. In: IEEE/IAPR 2nd International Conference on Biometrics ICB (2007), doi:10.1007/978-3-540-74549-5\_76Google Scholar
  5. 5.
    Nikam, S.B., Aggarwal, S.: Local binary pattern and wavelet-based spoof fingerprint detection. International Journal of Biometrics 1(2), 141–159 (2008)CrossRefGoogle Scholar
  6. 6.
    Coli, P., Marcialis, G.L., Roli, F.: Power spectrum-based fingerprint vitality detection. In: IEEE Int. Work. on Automatic Identification Advanced Technologies AutoID 2007, pp. 169–173 (2007)Google Scholar
  7. 7.
    Nikam, S.B., Aggarwal, S.: Wavelet energy signature and GLCM features-based fingerprint anti-spoofing. In: IEEE Int. Conf. On Wavelet Analysis and Pattern Recognition (2008), doi:10.1109/ICWAPR.2008.4635872Google Scholar
  8. 8.
    Abyanka, A., Schuckers, S.: Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recognition 42, 452–464 (2009)CrossRefGoogle Scholar
  9. 9.
    Nikam, S.B., Agarwal, S.: Fingerprint Liveness Detection Using Curvelet Energy and Co-occurrence Signatures. In: IEEE Fifth International Conference on Computer Graphics, Imaging and Visualization (2008) doi:10.1109/CGIV.2008.9Google Scholar
  10. 10.
    Yambay, D., et al.: LivDet 2011 - Fingerprint Liveness Detection Competition 2011. In: 5th IAPR/IEEE Int. Conf. on Biometrics (ICB 2012), pp. 208–215 (2012), doi:10.1109/ICB.2012.6199810Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gian Luca Marcialis
    • 1
  • Luca Ghiani
    • 1
  • Katja Vetter
    • 2
  • Dirk Morgeneier
    • 2
  • Fabio Roli
    • 1
  1. 1.Department of Electrical and Electronic EngineeringUniversity of CagliariItaly
  2. 2.Crossmatch Technologies Inch.USA

Personalised recommendations