Advertisement

Score Level Fusion Scheme in Hybrid Multibiometric System

  • Saliha Artabaz
  • Layth SlimanEmail author
  • Karima Benatchba
  • Hachemi Nabil Dellys
  • Mouloud Koudil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)

Abstract

Multibiometric systems are a promising area that addresses a number of unimodal biometric systems drawbacks. The main limit of these systems is the lack of information in terms of quantity (number of discriminant features) and quality (diversity of information, correlation…). Using multiple sources of information and/or treatment is a solution to overcome these problems and enhance system performances. Performance requirements of current systems related to context use involve designed solutions that optimally satisfy security requirements. This can represent an optimization problem that aims at searching the optimal solution matching security needs. In our study, we are interested in combining different score level rules using an evolutionary algorithm. We use Genetic Algorithm to derive a score fusion function based on primitive operations. The process uses an optimized tree to determine function structure. We perform experiments on the XM2VTS score database based on a well-founded protocol for reliable results. The obtained results are promising and outperforms other fusion rules.

Keywords

Score level fusion Multibiometric Genetic algorithm Tree representation 

References

  1. 1.
    Alajlan, N., Saiful Islam, M., Ammour, N.: Fusion of fingerprint and heartbeat biometrics using fuzzy adaptive genetic algorithm. In: World Congress on Internet Security, pp. 76–81 (2013)Google Scholar
  2. 2.
    Alsaade, F., Ariyaeeinia, A., Malegaonkar, A., Pillay, S.: Qualitative fusion of normalised scores in multimodal biometrics. Pattern Recognit. Lett. 30(5), 564–569 (2009)CrossRefGoogle Scholar
  3. 3.
    Anzar, S.T.M., Sathidevi, P.S.: On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics. EURASIP J. Adv. Signal Process. 10, 1–17 (2014)Google Scholar
  4. 4.
    Barbosa, I.B., Theoharis, T., Schellewald, C., Athwal, C.: Transient biometrics using finger nails. In: Proceedings of 6th Biometrics: Theory, Applications and Systems (BTAS), 2013, pp. 1–6. Arlington, VA, USA, 29 Sept–2 Oct 2013Google Scholar
  5. 5.
    Bendris, M., Charlet, D., Chollet, G.: Introduction of quality measures in audio-visual identity verification. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1913–1916, Taipei, 19–24 Apr 2009Google Scholar
  6. 6.
    Bengio, S., Mariethoz, J.: The expected performance curve: a new assessment measure for person authentication. In: The Speaker and Language Recognition Workshop (Odyssey), pp. 279–284 (2004)Google Scholar
  7. 7.
    Eskandaria, M., Toygar, Ö.: Selection of optimized features and weights on face-iris fusion using distance images. Comput. Vis. Image Underst. 137, 63–75 (2015)CrossRefGoogle Scholar
  8. 8.
    Giot, R., Rosenberger, C.: Genetic programming for multibiometrics. Expert Syst. Appl. 39, 1837–1847 (2012)CrossRefGoogle Scholar
  9. 9.
    Kryszczuk, K., Richiardi, J., Prodanov, P., Drygajlo, A.: Reliability-based decision fusion in multimodal biometric verification systems. EURASIP J. Appl. Signal Process. 2007(1), 74 (2007)Google Scholar
  10. 10.
    Kumar, A., Kanhangad, V., Zhang, D.: A new framework for adaptive multimodal biometrics management. Inf. Forensics Secur. 5(1), 92–102 (2010)CrossRefGoogle Scholar
  11. 11.
    Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Inf. Forensics Secur. 4, 98–110 (2009)CrossRefGoogle Scholar
  12. 12.
    Morizet, N., Gilles, J.: A new adaptive combination approach to score level fusion for face and iris biometrics combining wavelets and statistical moments. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 661–671. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Pal, S., Mukherjee, K., Majumder, B.P., Saha, C., Panigrahi, B.K., Das, S.: Differential evolution based score level fusion for multi-modal biometric systems. In: Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 38–44. Orlando, FL, USA, 9–12 Dec 2014)Google Scholar
  14. 14.
    Parviz, M., Moin, M.S.: Boosting Approach for score level fusion in multimodal biometrics based on AUC maximization. J. Inf. Hiding Multimedia Signal Process. 2(1), 51–59 (2011)Google Scholar
  15. 15.
    Poh, N., Bengio, S.: Database, protocol and tools for evaluating score-level fusion algorithms in biometric authentication. Pattern Recogn. 39(2), 223–233 (2006)CrossRefGoogle Scholar
  16. 16.
    Poh, N., Bengio, S.: Improving fusion with margin-derived confidence in biometric authentication tasks. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 474–483. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Ross, A., Nandakumar, K., Jain, A.: Handbook of Multibiometrics. Springer, Heidelberg (2006)Google Scholar
  18. 18.
    Souvannavong, F., Merialdo, B., Huet, B.: Multi-modal classifier fusion for video shot content retrieval. In: Proceedings of WIAMIS (Eurecom, 8 avril 2005)Google Scholar
  19. 19.
    Srinivas, N., Veeramachaneni, K., Osadciw, L.A.: Fusing correlated data from multiple classifiers for improved biometric verification. In: Proceedings of 9th Information Fusion, pp. 1504–1511. Seattle, WA, USA, 6–9 July 2009Google Scholar
  20. 20.
    Kale, K.V., Rode, Y.S., Kazi, M.M., Dabhade, S.B., Chavan, S.V.: Multimodal biometric system using fingernail and finger knuckle. In: Computational and Business Intelligence (ISCBI), pp. 279–283. New Delhi, India, 24–26 Aug 2013Google Scholar
  21. 21.
    Unar, J.A., Seng, W.C., Abbasi, A.: A review of biometric technology along with trends and prospects. Pattern Recogn. 47, 279–283 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Saliha Artabaz
    • 1
  • Layth Sliman
    • 2
    Email author
  • Karima Benatchba
    • 1
  • Hachemi Nabil Dellys
    • 1
  • Mouloud Koudil
    • 1
  1. 1.Ecole nationale Supérieure d’Informatique ESIOued-Smar, AlgerAlgeria
  2. 2.Ecole d’ingénieur généraliste en informatique et technologies du numérique EfreiVillejuif, ParisFrance

Personalised recommendations