Fingerprint Presentation Attack Detection Method Based on a Bag-of-Words Approach

  • Lázaro Janier González-Soler
  • Leonardo Chang
  • José Hernández-Palancar
  • Airel Pérez-Suárez
  • Marta Gomez-Barrero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

Fingerprint-based biometric systems are not entirely secure due to their vulnerability to presentation attacks. In this paper, we propose a new presentation attack method based on a Bag-of-Words approach, which by combining local and global information of fingerprint can correctly identify bona fine presentations from presentation attacks. The experimental evaluation of our proposal, over the well-known LivDet 2011 dataset, showed an Average Classification Error of \(4.73\%\), outperforming the state of the art.

Notes

Acknowledgement

This work was partly supported by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within the Center for Research in Security and Privacy (CRISP).

References

  1. 1.
    Marasco, E., Ross, A.: A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput. Surv. (CSUR) 47(2), 28 (2015)Google Scholar
  2. 2.
    Sousedik, C., Busch, C.: Presentation attack detection methods for fingerprint recognition systems: a survey. Iet Biometrics 3(4), 219–233 (2014)CrossRefGoogle Scholar
  3. 3.
    Ghiani, L., Marcialis, G.L., Roli, F.: Fingerprint liveness detection by local phase quantization. In: ICPR 2012, pp. 537–540. IEEE (2012)Google Scholar
  4. 4.
    Jia, X., Yang, X., Zang, Y., Zhang, N., Dai, R., Tian, J., Zhao, J.: Multi-scale block local ternary patterns for fingerprints vitality detection. In: ICB 2013, pp. 1–6. IEEE (2013)Google Scholar
  5. 5.
    Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: Fingerprint liveness detection based on Weber local image descriptor. In: 2013 IEEE Workshop on BIOMS, pp. 46–50. IEEE (2013)Google Scholar
  6. 6.
    Ghiani, L., Hadid, A., Marcialis, G.L., Roli, F.: Fingerprint liveness detection using binarized statistical image features. In: BTAS, pp. 1–6. IEEE (2013)Google Scholar
  7. 7.
    Dubey, R.K., Goh, J., Thing, V.L.: Fingerprint liveness detection from single image using low-level features and shape analysis. IEEE Trans. Inf. Forensics Secur. 11(7), 1461–1475 (2016)CrossRefGoogle Scholar
  8. 8.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop ECCV, pp. 1–22 (2004)Google Scholar
  9. 9.
    ISO: Information technology biometric presentation attack detection part 3: Testing and reporting, JTC 1/SC 37, Geneva, Switzerland ISO/IEC FDIS 30107–3:2017 (2017)Google Scholar
  10. 10.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE International Conference on Computer Vision (2007)Google Scholar
  11. 11.
    Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008). http://www.vlfeat.org/
  12. 12.
    Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. Pattern Anal. Mach. Intellingence 34(3), 480–492 (2011)CrossRefGoogle Scholar
  13. 13.
    Yambay, D., Ghiani, L., Denti, P., Marcialis, G.L., Roli, F., Schuckers, S.: Livdet 2011-fingerprint liveness detection competition 2011. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 208–215. IEEE (2012)Google Scholar
  14. 14.
    Nogueira, R.F., de Alencar Lotufo, R., Machado, R.C.: Evaluating software-based fingerprint liveness detection using convolutional networks and local binary patterns. In: 2014 IEEE Workshop on BIOMS Proceedings, pp. 22–29. IEEE (2014)Google Scholar
  15. 15.
    Xia, Z., Lv, R., Zhu, Y., Ji, P., Sun, H., Shi, Y.-Q.: Fingerprint liveness detection using gradient-based texture features. Signal Image Video Process. 11(2), 381–388 (2017)CrossRefGoogle Scholar
  16. 16.
    Xia, Z., Yuan, C., Sun, X., Sun, D., Lv, R.: Combining wavelet transform and LBP related features for fingerprint liveness detection. IAENG Int. J. Comput. Sci. 43(3), 290–298 (2016)Google Scholar
  17. 17.
    Jiang, Y., Liu, X.: Spoof fingerprint detection based on co-occurrence matrix. Int. J. SERSC 8(8), 373–384 (2015)Google Scholar
  18. 18.
    Schuckers, S., Johnson, P.: Fingerprint pore analysis for liveness detection. US Patent App. 14/243,420, 2 April 2014Google Scholar
  19. 19.
    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. Inf. Sci. 268, 91–102 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Advanced Technologies Application Center (CENATAV)HavanaCuba
  2. 2.da/sec - Biometrics and Internet Security Research GroupHochschule DarmstadtDarmstadtGermany
  3. 3.Tecnologico de MonterreyAtizapán de ZaragozaMexico

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