Selection of Optimal Dimensionality Reduction Methods for Face Recognition Using Genetic Algorithms

  • Önsen Toygar
  • Adnan Acan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3261)


A new approach for optimal selection of dimensionality reduction methods for individual classifiers within a multiple classifier system is introduced for the face recognition problem. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are used as the appearance-based statistical methods for dimensionality reduction. A face is partitioned into five segments and each segment is processed by a particular dimensionality reduction method. This results in a low-complexity divide-and-conquer approach, implemented as a multiple-classifier system where distance-based individual classifiers are built using appearance-based statistical methods. The decisions of individual classifiers are unified by an appropriate combination method. Genetic Algorithms (GAs) are used to select the optimal dimensionality reduction method for each individual classifier. Experiments are conducted to show that the proposed approach outperforms the holistic methods.


Face Recognition Linear Discriminant Analysis Independent Component Analysis Face Image Recognition Performance 
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 2004

Authors and Affiliations

  • Önsen Toygar
    • 1
  • Adnan Acan
    • 1
  1. 1.Computer Engineering DepartmentEastern Mediterranean UniversityGazimağusa, T.R.N.C.Turkey

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