Towards Predicting Optimal Fusion Candidates: A Case Study on Biometric Authentication Tasks

  • Norman Poh
  • Samy Bengio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3361)


Combining multiple information sources, typically from several data streams is a very promising approach, both in experiments and to some extend in various real-life applications. However, combining too many systems (base-experts) will also increase both hardware and computation costs. One way to selecting a subset of optimal base-experts out of N is to carry out the experiments explicitly. There are 2 N –1 possible combinations. In this paper, we propose an analytical solution to this task when weighted sum fusion mechanism is used. The proposed approach is at least valid in the domain of person authentication. It has a complexity that is additive between the number of examples and the number of possible combinations while the conventional approach, using brute-force experimenting, is multiplicative between these two terms. Hence, our approach will scale better with large fusion problems. Experiments on the BANCA multi-modal database verified our approach. While we will consider here fusion in the context of identity verification via biometrics, or simply biometric authentication, it can also have an important impact in meetings because this a priori information can assist in retrieving highlights in meeting analysis as in “who said what”. Furthermore, automatic meeting analysis also requires many systems working together and involves possibly many audio-visual media streams. Development in fusion of identity verification will provide insights into how fusion in meetings can be done. The ability to predict fusion performance is another important step towards understanding the fusion problem.


Equal Error Rate Optimal Subset Fusion Experiment False Acceptance Rate Biometric Authentication 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kittler, J., Messer, K., Czyz, J.: Fusion of Intramodal and Multimodal Experts in Personal Identity Authentication Systems. In: Proc. Cost 275 Workshop, Rome, pp. 17–24 (2002)Google Scholar
  2. 2.
    Poh, N., Bengio, S.: Why Do Multi-Stream, Multi-Band and Multi-Modal Approaches Work on Biometric User Authentication Tasks? In: IEEE Int’l Conf. Acoustics, Speech, and Signal Processing (ICASSP), Montreal, vol. V, pp. 893–896 (2004)Google Scholar
  3. 3.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1999)Google Scholar
  4. 4.
    Bailly-Baillière, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariéthoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., Ruiz, B., Thiran, J.-P.: The BANCA Database and Evaluation Protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Marcel, C.: Multimodal Identity Verification at IDIAP, Communication Report 03-04, IDIAP, Martigny, Switzerland (2003)Google Scholar
  6. 6.
    Poh, N., Bengio, S.: How Do Correlation and Variance of Base Classifiers Affect Fusion in Biometric Authentication Tasks?, Research Report 04-18, IDIAP, Martigny, Switzerland (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Norman Poh
    • 1
  • Samy Bengio
    • 1
  1. 1.IDIAP Research InstituteMartignySwitzerland

Personalised recommendations