Optimization of Face Relevance Maps with Total Classification Error Minimization

  • Michal Kawulok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)


This paper presents a concept of optimizing parameters used for solving image identification tasks developed during research aimed at improving recognition of human face images. Effectiveness of closed-set identification is measured in a form of Total Classification Error (TCE) which can be expressed as a function of parameters used for calculating similarity between samples. TCE can be minimized for a defined training set in order to obtain optimal values of the parameters. This method was implemented to optimize face relevance maps applied to improve the Eigenfaces method for human face recognition. Results of the experiments presented in this paper confirm effectiveness of the developed approach.


Feature Vector Face Recognition Face Image Face Recognition System Partial Error 
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 2008

Authors and Affiliations

  • Michal Kawulok
    • 1
  1. 1.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

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