Confidence Based Rank Level Fusion for Multimodal Biometric Systems

  • Hossein TalebiEmail author
  • Marina L. Gavrilova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


Multimodal biometric systems have proven advantages over single biometric systems as they are using multiple traits of users. The intra-class variance provided by using more than one trait results in a high identification rate. Still, one of the missing parts in a multimodal system is inattention to the discriminability of each rank list for each specific user. This paper introduces a novel approach to select a combination of rank lists in rank level so that it provides the highest discrimination for any specific query. The rank list selection is based on pseudo-scores lists that are created by combination of rank lists and resemblance probability distribution of users. The experimental results on a multimodal biometric system based on frontal face, profile face, and ear indicated higher identification rate by using novel confidence based rank level fusion.


Multimodal biometrics Rank level fusion Rank list selection Resemblance probability distribution 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of multibiometrics. Springer Science & Business Media (2006)Google Scholar
  2. 2.
    Jain, A.K., Flynn, P., Ross, A.A.: Handbook of biometrics. Springer Science & Business Media (2007)Google Scholar
  3. 3.
    Revett, K.: Behavioral biometrics: a remote access approach. John Wiley & Sons (2008)Google Scholar
  4. 4.
    Prabhakar, S., Jain, A.K.: Decision-level fusion in fingerprint verification. Pattern Recognition 35(4), 861–874 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Mowar, M., Gavrilova, M.L.: Multimodal biometric system using rank-level fusion approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(4), 867–878 (2009)CrossRefGoogle Scholar
  6. 6.
    Bhatnagar, J., Kumar, A., Saggar, N.: A novel approach to improve biometric recognition using rank level fusion. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp 1–6. IEEE (2007)Google Scholar
  7. 7.
    Kumar, A., Shekhar, S.: Personal identification using multibiometrics rank-level fusion. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 41(5), 743–752 (2011)CrossRefGoogle Scholar
  8. 8.
    Lee, Y.-J., Lee, K.-H., Jee, H., Gil, Y.H., Choi, W., Ahn, D., Pan, S.B.: Fusion for multimodal biometric identification. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1071–1079. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  9. 9.
    Monwar, M.M., Gavrilova, M., Wang, Y.: A novel fuzzy multimodal information fusion technology for human biometric traits identification. In: 2011 10th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), pp. 112–119. IEEE (2011)Google Scholar
  10. 10.
    Marasco, E., Abaza, A., Cukic, B.: Why rank-level fusion? and what is the impact of image quality?Google Scholar
  11. 11.
    Abaza, A., Ross, A.: Quality based rank-level fusion in multibiometric systems. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS 2009, pp. 1–6. IEEE (2009)Google Scholar
  12. 12.
    Alam, M.R., Bennamoun, M., Togneri, R., Sohel, F.: Confidence-based rank-level fusion for audio-visual person identification system. In: 3rd International Conference on Pattern Recognition Applications and Methods, 2014, pp. 608–615 (2014)Google Scholar
  13. 13.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  14. 14.
    Talebi, H., Gavrilova, M.: Prior resemblance probability of users for multimodal biometrics rank fusion. In: IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015). IEEE (2015)Google Scholar
  15. 15.
    Monwar, M., Gavrilova, M.: Fes: a system for combining face, ear and signature biometrics using rank level fusion. In: Fifth International Conference on Information Technology: New Generations, 2008, pp. 922–927. IEEE (2008)Google Scholar
  16. 16.
    Bhattacharyya, A.: On a measure of divergence between two multinomial populations. Sankhyā: The Indian Journal of Statistics, 401–406 (1946)Google Scholar
  17. 17.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  18. 18.
    USTB ear database, china. (accessed May 11, 2008)
  19. 19.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of CalgaryCalgaryCanada

Personalised recommendations