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. The evaluation has been done using two methodologies; first with no rejection criteria, and then with rejection criteria By applying the rejection criteria an equal error rate of 3.5 % is obtained for AT & T database and 6 % is obtained for Yale database Experimental results also show the face recognition using the proposed codebook with no rejection criteria 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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Varghese, E.B., Wilscy, M. (2011). Vector Quantization Based Face Recognition Using Integrated Adaptive Fuzzy Clustering. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Parallel Distributed Computing. PDCTA 2011. Communications in Computer and Information Science, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24037-9_4
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DOI: https://doi.org/10.1007/978-3-642-24037-9_4
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