Error-Rate Based Biometrics Fusion

  • Kar-Ann Toh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


This paper addresses the face verification problem by fusing visual and infra-red face verification systems. Unlike the conventional least squares error minimization approach which involves fitting of a learning model to data density and then perform a threshold process for error counting, this work directly formulates the required target error count rate in terms of design model parameters. A simple power series model is adopted as the fusion classifier and our experiments show promising results.


Face Recognition Face Image Equal Error Rate False Acceptance Rate False Rejection Rate 
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 2007

Authors and Affiliations

  • Kar-Ann Toh
    • 1
  1. 1.Biometrics Engineering Research Center, School of Electrical & Electronic Engineering, Yonsei University, SeoulKorea

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