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Neural Network for Winner take All Competition using Palm Print Recognization


K. Karthikeyan, L. Anusha,E. Janani, K. Mekala


Vol. 15  No. 3  pp. 91-95


Winner take all competition (WTA) widely takes place in many application to predict the winner of the participants. Many mathematical models are proposed to describe the phenomena discovered in different fields. It’s is often difficult to explain the underlying mechanism of such a competition from the perspective of the feedback based on sophisticated models. Existing system do not have database also their accuracy and complexity is less. In this paper we present a simple form, which produces the WTA by taking advantages of selective positive negative feedback through interaction of neurons and also we have used the palm print images of the individuals in order to extract the features. For the feature extraction we use adaptive histogram equalization (AHE) and Gray layer co-occurrence matrix algorithm (GLCM).line feature points are used for the feature extraction by recognizing the vein.


Winner take all, competition, selective positive negative feedback, recurrent neural network, adaptive, Histogram equalization (AHE), Gray layer co-occurrence matrix algorithm (GLCM).