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Performance Anchored Score Normalization for Multi-biometric Fusion

  • Naser Damer
  • Alexander Opel
  • Alexander Nouak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8034)

Abstract

This work presents a family of novel normalization techniques for score-level multi-biometric fusion. The proposed normalization is not only concerned to bring comparison scores to a common range and scale, it also focuses in bringing certain operational performance points in the distribution into alignment. The Performance Anchored Normalization (PAN) algorithms discussed here were tested on the extended Multi Modal Verification for Teleservices and Security applications database (XM2VTS) and proved to outperform conventional score normalization techniques in most tests. The tests were performed with combination fusion rules and presented as biometric verification performance measures.

Keywords

Gaussian Mixture Model Normalization Technique Combination Rule Equal Error Rate Median Absolute Deviation 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Naser Damer
    • 1
  • Alexander Opel
    • 1
  • Alexander Nouak
    • 1
  1. 1.Fraunhofer Institute for Computer Graphics Research IGDDarmstadtGermany

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