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A Fuzzy Neuro Clustering Based Vector Quantization for Face Recognition

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Advances in Computing and Communications (ACC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 192))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, 695581, Kerala, India

    Elizabeth B. Varghese & M. Wilscy

Authors
  1. Elizabeth B. Varghese
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  2. M. Wilscy
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Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Auburn, 98071-2259, Washington, USA

    Ajith Abraham

  2. Departamento de Comunicaciones, Universidad Politcnica de Valencia, 46071, Valencia, Spain

    Jaime Lloret Mauri

  3. Avaya Labs Research, Basking Ridge, NJ, USA

    John F. Buford

  4. University of Massachusetts, 100 Morrissey Blvd., 02125-3393, Boston, MA, USA

    Junichi Suzuki

  5. Rajagiri School of Engineering and Technology, Rajagiri Valley, Kakkanad, 682 039, Kochi, India

    Sabu M. Thampi

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© 2011 Springer-Verlag Berlin Heidelberg

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Varghese, E.B., Wilscy, M. (2011). A Fuzzy Neuro Clustering Based Vector Quantization for Face Recognition. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_40

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  • DOI: https://doi.org/10.1007/978-3-642-22720-2_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22719-6

  • Online ISBN: 978-3-642-22720-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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