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A Hierarchical Face Recognition Algorithm

  • Remco R. Bouckaert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5828)

Abstract

In this paper, we propose a hierarchical method for face recognition where base classifiers are defined to make predictions based on various different principles and classifications are combined into a single prediction. Some features are more relevant to particular face recognition tasks than others. The hierarchical algorithm is flexible in selecting features relevant for the face recognition task at hand. In this paper, we explore various features based on outline recognition, PCA classifiers applied to part of the face and exploitation of symmetry in faces. By combining the predictions of these features we obtain superior performance on benchmark datasets (99.25% accuracy on the ATT dataset) at reduced computation cost compared to full PCA.

Keywords

Face Recognition Linear Discriminant Analysis Independent Component Analysis Principle Component Analysis Independent Component Analysis 
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

  • Remco R. Bouckaert
    • 1
  1. 1.Computer Science DepartmentUniversity of WaikatoNew Zealand

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