On Multiview Analysis for Fingerprint Liveness Detection

  • Amirhosein Toosi
  • Sandro Cumani
  • Andrea BottinoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)


Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literature.


Spoofing detection Multiview approach SVM Multi task learning Sparse reconstruction 


  1. 1.
    International Business Time: iphone 6 touch id fingerprint scanner hacked days after launch (2015). (Accessed June 01, 2015)
  2. 2.
    Marasco, E., Ross, A.: A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput. Surv. 47(2), 28:1–28:36 (2014)CrossRefGoogle Scholar
  3. 3.
    Yambay, D., Ghiani, L., Denti, P., Marcialis, G., Roli, F., Schuckers, S.: Livdet 2011 - fingerprint liveness detection competition 2011. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 208–215, March 2012Google Scholar
  4. 4.
    Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, S.: Impact of artificial “gummy” fingers on fingerprint systems. In: Proceedings of SPIE 4677, January 2002Google Scholar
  5. 5.
    Antonelli, A., Cappelli, R., Maio, D., Maltoni, D.: Fake finger detection by skin distortion analysis. IEEE Transactions on Information Forensics and Security 1(3), 360–373 (2006)CrossRefGoogle Scholar
  6. 6.
    Schuckers, S.A.C., Parthasaradhi, S.T.V., Derakshani, R., Hornak, L.A.: Comparison of classification methods for time-series detection of perspiration as a liveness test in fingerprint devices. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 256–263. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  7. 7.
    Abhyankar, A., Schuckers, S.: Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques. In: 2006 IEEE International Conference on Image Processing, pp. 321–324, October 2006Google Scholar
  8. 8.
    Nikam, S., Agarwal, S.: Co-occurrence probabilities and wavelet-based spoof fingerprint detection. Int. Journal of Image and Graphics 09(02), 171–199 (2009)CrossRefGoogle Scholar
  9. 9.
    Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: Local contrast phase descriptor for fingerprint liveness detection. Pattern Recognition 48(4), 1050–1058 (2015)CrossRefGoogle Scholar
  10. 10.
    Nikam, S., Agarwal, S.: Texture and wavelet-based spoof fingerprint detection for fingerprint biometric systems. In: First International Conference on Emerging Trends in Engineering and Technology, ICETET 2008, pp. 675–680, July 2008Google Scholar
  11. 11.
    Jia, X., Yang, X., Cao, K., Zang, Y., Zhang, N., Dai, R., Zhu, X., Tian, J.: Multi-scale local binary pattern with filters for spoof fingerprint detection. Information Sciences 268, 91–102 (2014)CrossRefGoogle Scholar
  12. 12.
    Ghiani, L., Marcialis, G., Roli, F.: Fingerprint liveness detection by local phase quantization. In: ICPR 2012, pp. 537–540, November 2012Google Scholar
  13. 13.
    Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: Fingerprint liveness detection based on weber local image descriptor. In: IEEE BIOMS 2013, pp. 46–50, September 2013Google Scholar
  14. 14.
    Ghiani, L., Hadid, A., Marcialis, G., Roli, F.: Fingerprint liveness detection using binarized statistical image features. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6, September 2013Google Scholar
  15. 15.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  16. 16.
    Yuan, X.T., Yan, S.: Visual classification with multi-task joint sparse representation. CVPR 2010, 3493–3500, June 2010Google Scholar
  17. 17.
    Schmidt, M., Fung, G., Rosales, R.: Fast optimization methods for l1 regularization: a comparative study and two new approaches. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 286–297. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  18. 18.
    Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(3), 503–528 (1989)zbMATHMathSciNetCrossRefGoogle Scholar
  19. 19.
    Brummer, N.: Fusion of heterogeneous speaker recognition systems in the STBU submission for the NIST speaker recognition evaluation 2006. IEEE Transactions on Audio, Speech, and Language Processing 15(7), 2072–2084 (2006)CrossRefGoogle Scholar
  20. 20.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005, vol. 1, pp. 886–893, June 2005Google Scholar
  21. 21.
    Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Systems with Applications 39(3), 3634–3641 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Amirhosein Toosi
    • 1
  • Sandro Cumani
    • 1
  • Andrea Bottino
    • 1
    Email author
  1. 1.Politecnico di TorinoTurinItaly

Personalised recommendations