Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Fusion, Score-Level

  • Arun Ross
  • Karthik Nandakumar
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_158


Fusion at the confidence level; Fusion at the measurement level;  Match score fusion


In score-level fusion the match scores output by multiple biometric matchers are consolidated in order to render a decision about the identity of an individual. Typically, this consolidation procedure results in the generation of a single scalar score which is subsequently used by the biometric system. Fusion at this level is the most commonly discussed approach in the biometric literature primarily due to the ease of accessing and processing match scores (compared with the raw biometric data or the feature set extracted from the data). Fusion methods at this level can be broadly classified into three categories: density-based schemes, transformation-based schemes and classifier-based schemes.


A match score is the result of comparing two feature sets extracted using the same feature extractor. A similarity score denotes how “similar” the two feature sets are, while a distance...

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  1. 1.
    Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. 1st edn. Springer, New York, USA (2006)Google Scholar
  2. 2.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)MATHGoogle Scholar
  3. 3.
    Kittler, J., Hatef, M., Duin, R.P., Matas, J.G.: On combining classifiers. IEEE Trans. Pattern Analy. Mach. Intell. 20, 226–239 (1998)CrossRefGoogle Scholar
  4. 4.
    Dass, S.C., Nandakumar, K., Jain, A.K.: A principled approach to score level fusion in multimodal biometric systems. In: Proceedings of Fifth International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), pp. 1049–1058. Rye Brook, USA (2005)Google Scholar
  5. 5.
    Cappelli, R., Maio, D., Maltoni, D.: Combining fingerprint classifiers. In: Proceedings of First International Workshop on Multiple Classifier Systems, pp. 351–361. Cagliari, Italy (2000)Google Scholar
  6. 6.
    Snelick, R., Uludag, U., Mink, A., Indovina, M., Jain, A.K.: Large Scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans. Pattern Analy. Mach. Intell. 27, 450–455 (2005)CrossRefGoogle Scholar
  7. 7.
    Hampel, F.R., Rousseeuw, P.J., Ronchetti, E.M., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Functions. Wiley, New York (1986)MATHGoogle Scholar
  8. 8.
    Mosteller, F., Tukey, J.W.: Data Analysis and Regression: A Second Course in Statistics. Addison-Wesley, Reading, MA, USA (1977)Google Scholar
  9. 9.
    Brunelli, R., Falavigna, D.: Person Identification using multiple cues. IEEE Trans. Pattern Analy. Mach. Intell. 17, 955–966 (1995)CrossRefGoogle Scholar
  10. 10.
    Verlinde, P., Cholet, G.: Comparing decision fusion paradigms using k-NN based classifiers, decision trees and logistic regression in a multi-modal identity verification application. In: Proceedings of Second International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA), pp. 188–193. Washington D.C., USA (1999)Google Scholar
  11. 11.
    Chatzis, V., Bors, A.G., Pitas, I.: Multimodal decision-level fusion for person authentication. IEEE Trans. Syst. Man Cybernet. Part A: Syst. Humans 29, 674–681 (1999)CrossRefGoogle Scholar
  12. 12.
    Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Fusion of face and speech data for person identity verification. IEEE Trans. Neural Networks 10, 1065–1075 (1999)CrossRefGoogle Scholar
  13. 13.
    Bigun, E.S., Bigun, J., Duc, B., Fischer, S.: Expert Conciliation for multimodal person authentication systems using bayesian statistics. In: First International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), pp. 291–300. Crans-Montana, Switzerland (1997)Google Scholar
  14. 14.
    Sanderson, C., Paliwal, K.K.: Information Fusion and Person Verification Using Speech and Face Information. Research Paper IDIAP-RR 02-33, IDIAP (2002)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Arun Ross
    • 1
  • Karthik Nandakumar
    • 2
  1. 1.West Virginia UniversityMorgantownUSA
  2. 2.Institute for Infocomm Research A* STARFusionopolisSingapore