Abstract
Given a still image or a video, a face recognition application identifies or verifies face images using a stored database of faces. In this paper a method for face recognition using a fuzzy neural network classifier based on the Integrated Adaptive Fuzzy Clustering (IAFC) method has been proposed. IAFC forms the cluster boundaries by a combined similarity measure and by integrating the advantages of the fuzzy c-means (FCM), the adaptive resonance theory, and a fuzzified Kohonen-type learning rule. The proposed system achieved a recognition rate of 98.75% and 99.39% for the AT & T and Yale databases respectively, which is better compared to the Back Propagation Neural Network (BPNN) system. Considering the rejection rate for the non-registrants, the system achieved an equal error rate of 3.7 % and 1.3% for the AT & T and the Yale databases respectively which is better compared to most of the existing systems.
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Pankaj, D.S., Wilscy, M. (2011). Face Recognition Using Fuzzy Neural Network Classifier. 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_6
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DOI: https://doi.org/10.1007/978-3-642-24037-9_6
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