Fusion in Multibiometric Identification Systems: What about the Missing Data?

  • Karthik Nandakumar
  • Anil K. Jain
  • Arun Ross
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Many large-scale biometric systems operate in the identification mode and include multimodal information. While biometric fusion is a well-studied problem, most of the fusion schemes have been implicitly designed for the verification scenario and cannot account for missing data (missing modalities or incomplete score lists) that is commonly encountered in multibiometric identification systems. In this paper, we show that likelihood ratio-based score fusion, which was originally designed for verification systems, can be extended for fusion in the identification scenario under certain assumptions. We further propose a Bayesian approach for consolidating ranks and a hybrid scheme that utilizes both ranks and scores to perform fusion in identification systems. We also demonstrate that the proposed fusion rules can handle missing information without any ad-hoc modifications. We observe that the recognition performance of the simplest rank level fusion scheme, namely, the highest rank method, is comparable to the performance of complex fusion strategies, especially when the goal is not to obtain the best rank-1 accuracy but to just retrieve the top few matches.


Recognition Rate Fusion Rule Fusion Scheme Biometric System Rank Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Federal Bureau of Investigation: Integrated Automated Fingerprint Identification System,
  2. 2.
    Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Tulyakov, S., Govindaraju, V.: Combining Matching Scores in Identification Model. In: Proceedings of ICDAR, Seoul, South Korea, August-September 2005, pp. 1151–1155 (2005)Google Scholar
  4. 4.
    Dinerstein, S., Dinerstein, J., Ventura, D.: Robust Multi-Modal Biometric Fusion via Multiple SVMs. In: IEEE Intl. Conf. on Systems, Man and Cybernetics, October 2007, pp. 1530–1535 (2007)Google Scholar
  5. 5.
    Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.K.: Likelihood Ratio Based Biometric Score Fusion. IEEE Trans. on PAMI 30(2), 342–347 (2008)Google Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Chichester (2001)Google Scholar
  7. 7.
    Ho, T.K., Hull, J.J., Srihari, S.N.: Decision Combination in Multiple Classifier Systems. IEEE Trans. on PAMI 16(1), 66–75 (1994)Google Scholar
  8. 8.
    Melnik, O., Vardi, Y., Zhang, C.H.: Mixed Group Ranks: Preference and Confidence in Classifier Combination. IEEE Trans. on PAMI 26(8), 973–981 (2004)Google Scholar
  9. 9.
    Moon, H., Phillips, P.J.: Computational and Performance Aspects of PCA-based Face Recognition Algorithms. Perception 30(5), 303–321 (2001)Google Scholar
  10. 10.
    Grother, P., Phillips, P.J.: Models of Large Population Recognition Performance. In: Proceedings of CVPR, Washington DC, USA, June/July 2004, vol. 2, pp. 68–75 (2004)Google Scholar
  11. 11.
    Bolle, R., Connell, J., Pankanti, S., Ratha, N., Senior, A.: The Relationship Between the ROC Curve and the CMC. In: Proceedings of Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID), Buffalo, USA, October 2005, pp. 15–20 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Karthik Nandakumar
    • 1
  • Anil K. Jain
    • 2
  • Arun Ross
    • 3
  1. 1.Institute for Infocomm Research, A*STARFusionopolisSingapore
  2. 2.Michigan State UniversityEast LansingUSA
  3. 3.West Virginia UniversityMorgantownUSA

Personalised recommendations