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Face Recognition Using DCT and Hierarchical RBF Model

  • Yuehui Chen
  • Yaou Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and Hierarchical Radial Basis Function Network (HRBF) classification model. The DCT is employed to extract the input features to build a face recognition system, and the HRBF is used to identify the faces. Based on the pre-defined instruction/operator sets, a HRBF model can be created and evolved. This framework allows input features selection. The HRBF structure is developed using Extended Compact Genetic Programming (ECGP) and the parameters are optimized by Differential Evolution (DE). Empirical results indicate that the proposed framework is efficient for face recognition.

Keywords

Face Recognition Discrete Cosine Transform Face Recognition System Gaussian Radial Basis Function Yale Face Database 
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 2006

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Yaou Zhao
    • 1
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R. China

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