Multimodal Biometric Fusion: A Study on Vulnerabilities to Indirect Attacks

  • Marta Gomez-Barrero
  • Javier Galbally
  • Julian Fierrez
  • Javier Ortega-Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


Fusion of several biometric traits has traditionally been regarded as more secure than unimodal recognition systems. However, recent research works have proven that this is not always the case. In the present article we analyse the performance and robustness of several fusion schemes to indirect attacks. Experiments are carried out on a multimodal system based on face and iris, a user-friendly trait combination, over the publicly available multimodal Biosecure DB. The tested system proves to have a high vulnerability to the attack regardless of the fusion rule considered. However, the experiments prove that not necessarily the best fusion rule in terms of performance is the most robust to the type of attack considered.


Security vulnerabilities multimodality iris recognition face recognition fusion schemes 


  1. 1.
    Jain, A.K., et al.: Biometrics: a tool for information security. IEEE TIFS 1(2), 125–143 (2006)Google Scholar
  2. 2.
    Schneier, B.: Inside risks: the uses and abuses of biometrics. Commun. ACM 42, 136 (1999)CrossRefGoogle Scholar
  3. 3.
    Galbally, J., et al.: Evaluation of direct attacks to fingerprint verification systems. Telecommunication Systems 47, 243–254 (2011)CrossRefGoogle Scholar
  4. 4.
    Galbally, J., et al.: On the vulnerability of face verification systems to hill-climbing attacks. PR 43, 1027–1038 (2010)zbMATHGoogle Scholar
  5. 5.
    Akhtar, Z., et al.: Robustness analysis of likelihood ratio score fusion rule for multimodal biometric systems under spoof attacks. In: Proc. ICCST, pp. 1–8 (2011)Google Scholar
  6. 6.
    TABULA RASA: Trusted biometrics under spoofing attacks (2013)Google Scholar
  7. 7.
    BEAT: Biometrics evaluation and testing (2013)Google Scholar
  8. 8.
    Rodrigues, R., et al.: Evaluation of biometric spoofing in a multimodal system. In: Proc. IEEE BTAS (September 2010) Google Scholar
  9. 9.
    Johnson, P.A., et al.: Multimodal fusion vulnerability to non-zero effort (spoof) imposters. In: Proc. WIFS (2010)Google Scholar
  10. 10.
    Akhtar, Z., et al.: Spoof attacks in mutimodal biometric systems. In: Proc. IPCSIT, vol. 4, pp. 46–51. IACSIT Press (2011)Google Scholar
  11. 11.
    Gomez-Barrero, M., et al.: Efficient software attack to multimodal biometric systems and its application to face and iris fusion. PRL (2013), doi:10.1016/j.patrec.2013.04.029Google Scholar
  12. 12.
    Masek, L., Kovesi, P.: Matlab source code for a biometric identification system based on iris patterns. Master’s thesis, University of Western Australia (2003)Google Scholar
  13. 13.
    Phillips, J., et al.: Overview of the face recognition grand challenge. In: Proc. IEEE CVPR, pp. 947–954 (2005)Google Scholar
  14. 14.
    Jain, A.K., et al.: Score normalization in multimodal biometric systems. PR 38, 2270–2285 (2005)Google Scholar
  15. 15.
    Kittler, J., et al.: On combining classifiers. IEEE TPAMI 20(3), 226–239 (1998)CrossRefGoogle Scholar
  16. 16.
    Fierrez, J.: Adapted Fusion Schemes for Multimodal Biometric Authentication. PhD thesis, Universidad Politecnica de Madrid (2006)Google Scholar
  17. 17.
    Ortega-Garcia, J., et al.: The multi-scenario multi-environment BioSecure multimodal database (BMDB). IEEE TPAMI 32, 1097–1111 (2010)CrossRefGoogle Scholar
  18. 18.
    ANSI-X9.84-2001: Biometric information management and security (2001)Google Scholar
  19. 19.
    Marfella, L., et al.: Liveness-based fusion approaches in multibiometrics. In: Proc. IEEE BIOMS (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marta Gomez-Barrero
    • 1
  • Javier Galbally
    • 1
  • Julian Fierrez
    • 1
  • Javier Ortega-Garcia
    • 1
  1. 1.Biometric Recognition Group–ATVS, EPSUniversidad Autonoma de MadridMadridSpain

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