Fusion of Fingerprint and Iris Biometrics Using Binary Ant Colony Optimization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)

Abstract

This paper presents an effective method for decision level fusion of fingerprint and iris biometrics using binary ant colony optimization (ACO) technique to identify the imposter instances. ACO is an evolutionary method. The selection of a proper set of optimization parameters for ACO is a multi-objective decision making optimization problem. Initially the matching scores for individual biometric classifiers are computed. Next, a ACO-based procedure is followed to simultaneously optimize the parameters and the fusion rules for fingerprint and iris biometrics. The proposed method has been found to perform satisfactorily on several benchmark datasets.

Keywords

ACO BACO Iris Fingerprint FAR FRR 

References

  1. 1.
    Kennedy, J., Eberhart, R.C., Shi, Y.H.: Swarm Intelligence. Morgan Kaufmann, CA (2001)Google Scholar
  2. 2.
    Gogoi, M., Bhattacharya, D.K.: An effective fingerprint classification method using minutiae score matching. J. Comput. Sci. Eng. 1(1) (2010)Google Scholar
  3. 3.
    Raghavendra, R., Rao A., Kumar G.H.: Multimodal biometric score fusion using gaussian mixture model and Monte Carlo method. J. Comput. Sci. Technol. 25(4), 771–782 (2010)Google Scholar
  4. 4.
    Singh, R., Vatsa, M., Noore, A.: Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recogn. 41(3), 880–893 (2008). Special Issue on Multimodal BiometricsCrossRefMATHGoogle Scholar
  5. 5.
    Nagar, A., Jain, A.K.: On the security of non-invertible fingerprint template transforms. In: Proceedings of IEEE Workshop on Information Forensics and Security, London, UK. (2009)Google Scholar
  6. 6.
    Rattani, A., Kisku, D.R., Bicego, M., Tistarelli, M.: Feature level fusion of face and fingerprint biometrics. In: Proceedings of 1st IEEE International Conference on Biometrics, Theory, Applications and Systems, pp. 1–6. (2007)Google Scholar
  7. 7.
    Giacinto G., Roli F.: Methods for dynamic classifier selection. In: 10th International Conference on Image Analysis and Processing, pp. 659–664 Venice, Italy. (1999)Google Scholar
  8. 8.
    Giacinto G., Roli F.: Selection of classifiers based on multiple classifier behaviour. In: Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition. (2000)Google Scholar
  9. 9.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas J.: On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. bfseries 20(3), 226–239 (1998)Google Scholar
  10. 10.
    Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Fusion strategies in multimodal biometric verification. In: IEEE International Conference on Multimedia and Expo, pp. 5–8. IEEE Computer Society, Los Alamitos, CA, USA (2003)Google Scholar
  11. 11.
    Rukhin, L., Malioutov, I.: Fusion of biometric algorithms in the recognition problem. Pattern Recogn. Lett. 26, 679–684 (2005)CrossRefGoogle Scholar
  12. 12.
    Veeramachaneni, K., Osadciw, L. A., Varshney, P. K.: Adaptive multimodal biometric fusion algorithm using particle swarm. SPIE 5099, 211–221 (2003)Google Scholar
  13. 13.
    Veeramachaneni, K., Osadciw1, L., Ross, A., Srinivas, N.: Decision-level fusion strategies for correlated biometric classifiers. In: Proceedings of IEEE Computer Society Workshop on Biometrics at the Computer Vision and Pattern Recogniton (CVPR) conference, Anchorage, USA (2008)Google Scholar
  14. 14.
    Dorigo, M., Thomas, S.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  15. 15.
    Duan, H. B.: Ant Colony Algorithms: Theory and Applications. Science Press, Beijing (2005)Google Scholar
  16. 16.
    Dorigo, M., Maniezzo, V., Colorni,A.: Ant system: Optimization by a colony of cooperating agents: IEEE Trans. Syst. Man Cybern. Part B 26, 29–41 (1996)Google Scholar
  17. 17.
    Dorigo,M., Caro,G.D., Stutzle,T.: Special issue on ant algorithms. Future Gener. Comput. Syst. 16, 851–871 (2000)Google Scholar
  18. 18.
  19. 19.
  20. 20.
    Daugman J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)Google Scholar
  21. 21.
    Hong, L., Jain, A.: Classification of fingerprint images. In: Proceedings of the 11th Scandinavian Conference on Image Analysis, Kangerlussuaq, Greenland. (1999)Google Scholar
  22. 22.

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of CSE/ITGIMTGuwahatiIndia
  2. 2.Department of CSETezpur UniversityAssamIndia

Personalised recommendations