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PCA and LDA Based Face Recognition Using Feedforward Neural Network Classifier

  • Alaa Eleyan
  • Hasan Demirel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are among the most common feature extraction techniques used for the recognition of faces. In this paper, two face recognition systems, one based on the PCA followed by a feedforward neural network (FFNN) called PCA-NN, and the other based on LDA followed by a FFNN called LDA-NN, are developed. The two systems consist of two phases which are the PCA or LDA preprocessing phase, and the neural network classification phase. The proposed systems show improvement on the recognition rates over the conventional LDA and PCA face recognition systems that use Euclidean Distance based classifier. Additionally, the recognition performance of LDA-NN is higher than the PCA-NN among the proposed systems.

Keywords

Principal Component Analysis Face Recognition Linear Discriminant Analysis Face Image Training Image 
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

  • Alaa Eleyan
    • 1
  • Hasan Demirel
    • 1
  1. 1.Department of Electrical and Electronic EngineeringEastern Mediterranean UniversityGazimağusa, North CyprusTurkey

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