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On Improving the Efficiency of Eigenface Using a Novel Facial Feature Localization

  • Aleksey Izmailov
  • Adam Krzyżak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

Face recognition is the most popular non-intrusive biometric technique with numerous applications in commerce, security and surveillance. Despite its good potential, most of the face recognition methods in the literature are not practical due to the lack of robustness, slow recognition, and semi-manual localizations. In this paper, we improve the robustness of eigenface-based systems with respect to variations in illumination level, pose and background. We propose a new method for face cropping and alignment which is fully automated and we integrate this method in Eigenface algorithm for face recognition. We also investigate the effect of various preprocessing techniques and several distance metrics on the overall system performance. The evaluation of this method under single-sample and multi-sample recognition is presented. The results of our comprehensive experiments on two databases, FERET and JRFD, show a significant gain compared to basic Eigenface method and considerable improvement with respect to recognition accuracy when compared with previously reported results in the literature.

Keywords

Face Recognition Face Image Mahalanobis Distance Face Detection Histogram Equalization 
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

  • Aleksey Izmailov
    • 1
  • Adam Krzyżak
    • 1
  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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