Adaptive Resonance Theory - Counter Propagation Neural Network for Iris Matching under Artifacts
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Abstract
A Biometric system is an automatic identification of an individual based on a unique feature or characteristic. Iris
recognition has great advantage such as variability, stability and security. Neural network have been shown to be technologically
powerful and flexible tool, ideally suited to perform identification analysis. Iris recognition system has include two steps : iris
localization and iris pattern matching .This paper present a Circular Hough transform method for iris localization which gives 99.8 %
accuracy in presence of artifacts like shadow, noise. Adaptive resonance theory - counter propagation network a fused version of ART
and CPN neural network is used for iris matching. It is based on concept of ART and CPN learning process. This network improves
the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer) over the other existing neural network like back
propagation, cascading feed forward network. The proposed algorithm improves the matching performance, learning speed and save
computation time.
Keywords: Iris; localization; normalization; Rubber sheet model; CPN; ART.
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