Robust Fusion: Extreme Value Theory for Recognition Score Normalization

  • Walter Scheirer
  • Anderson Rocha
  • Ross Micheals
  • Terrance Boult
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)


Recognition problems in computer vision often benefit from a fusion of different algorithms and/or sensors, with score level fusion being among the most widely used fusion approaches. Choosing an appropriate score normalization technique before fusion is a fundamentally difficult problem because of the disparate nature of the underlying distributions of scores for different sources of data. Further complications are introduced when one or more fusion inputs outright fail or have adversarial inputs, which we find in the fields of biometrics and forgery detection. Ideally a score normalization should be robust to model assumptions, modeling errors, and parameter estimation errors, as well as robust to algorithm failure. In this paper, we introduce the w-score, a new technique for robust recognition score normalization. We do not assume a match or non-match distribution, but instead suggest that the top scores of a recognition system’s non-match scores follow the statistical Extreme Value Theory, and show how to use that to provide consistent robust normalization with a strong statistical basis.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Walter Scheirer
    • 1
  • Anderson Rocha
    • 2
  • Ross Micheals
    • 3
  • Terrance Boult
    • 1
  1. 1.University of Colorado at Colorado Springs & Securics, Inc. 
  2. 2.Institute of ComputingUniversity of Campinas 
  3. 3.National Institute of Standards and Technology 

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