Semi-supervised Co-update of Multiple Matchers

  • Luca Didaci
  • Gian Luca Marcialis
  • Fabio Roli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)


Classification algorithms based on template matching are used in many applications (e.g., face recognition). Performances of template matching classifiers are obviously affected by the representativeness of available templates. In many real applications, such representativeness can substantially decrease over the time (e.g., due to “aging” effects in biometric applications). Among algorithms which have been recently proposed to deal with such issue, the template co-update algorithm uses the mutual help of two complementary template matchers to update the templates over the time in a semi-supervised way [8]. However, it can be shown that the template co-update algorithm derives from a more general framework which supports the use of more than two template matching classifiers. The aim of this paper is to point out this fact and propose the co-update of multiple matchers. Preliminary experimental results are shown to validate the proposed model.


Face Recognition Template Match Biometric System Fingerprint Image Initial Template 
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

  • Luca Didaci
    • 1
  • Gian Luca Marcialis
    • 2
  • Fabio Roli
    • 2
  1. 1.Department of Pedagogical and Philosophical SciencesUniversity of CagliariCagliariItaly
  2. 2.Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

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