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A Fuzzy Neuro Clustering Based Vector Quantization for Face Recognition

  • Elizabeth B. Varghese
  • M. Wilscy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)

Abstract

A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. In this paper, an improved codebook design method is proposed for Vector Quantization (VQ)-based face recognition which improves recognition accuracy. A codebook is created by combining a systematically organized codebook based on the classification of code patterns and another codebook created by Integrated Adaptive Fuzzy Clustering (IAFC) method. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database and Yale database. Experimental results show face recognition using the proposed codebook is more efficient yielding a rate of 99.25% for AT & T and 98.18% for Yale which is higher than most of the existing methods.

Keywords

Face Recognition Vector Quantization Codebook Integrated Adaptive Fuzzy Clustering Self Organization Map 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elizabeth B. Varghese
    • 1
  • M. Wilscy
    • 1
  1. 1.Department of Computer ScienceUniversity of KeralaThiruvananthapuramIndia

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