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Multibiometric People Identification: A Self-tuning Architecture

  • Maria De Marsico
  • Michele Nappi
  • Daniel Riccio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Multibiometric systems can solve a number of problems of unimodal approaches. One source for such problems can be found in the lack of dynamic update of parameters, which does not allow current systems to adapt to changes in the working settings. They are generally calibrated once and for all, so that they are tuned and optimized with respect to standard conditions. In this work we propose an architecture where, for each single-biometry subsystem, parameters are dynamically optimized according to the behaviour of all the others. This is achieved by an additional component, the supervisor module, which analyzes the responses from all subsystems and modifies the degree of reliability required from each of them to accept the respective responses. The paper explores two integration architectures with different interconnection degree, demonstrating that a tight component interaction increases system accuracy and allows identifying unstable subsystems.

Keywords

Face Recognition Recognition Rate Fusion Rule Equal Error Rate False Acceptance 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 2009

Authors and Affiliations

  • Maria De Marsico
    • 2
  • Michele Nappi
    • 1
  • Daniel Riccio
    • 1
  1. 1.Universitá Degli Studi di SalernoFisciano, SalernoItaly
  2. 2.Universitá Degli Studi di Roma - La SapienzaRomaItaly

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