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Confidence Based Rank Level Fusion for Multimodal Biometric Systems

  • Hossein TalebiEmail author
  • Marina L. Gavrilova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

Abstract

Multimodal biometric systems have proven advantages over single biometric systems as they are using multiple traits of users. The intra-class variance provided by using more than one trait results in a high identification rate. Still, one of the missing parts in a multimodal system is inattention to the discriminability of each rank list for each specific user. This paper introduces a novel approach to select a combination of rank lists in rank level so that it provides the highest discrimination for any specific query. The rank list selection is based on pseudo-scores lists that are created by combination of rank lists and resemblance probability distribution of users. The experimental results on a multimodal biometric system based on frontal face, profile face, and ear indicated higher identification rate by using novel confidence based rank level fusion.

Keywords

Multimodal biometrics Rank level fusion Rank list selection Resemblance probability distribution 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of CalgaryCalgaryCanada

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