An Adaptive Iris Recognition System with Aid of Local Histogram and Optimized FFBNN-AAPSO

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Iris recognition is the process of recognizing a person by analyzing the apparent pattern of his or her iris. Many techniques have been developed for iris recognition so far. A method that has been recently developed uses local histogram and image statistics. But it failed to locate the inner and the outer boundaries of the iris in the presence of dense eyelashes which results in poor pupil and iris localization. It also has high computational complexity which reduces the performance of the system. Here we propose a new iris recognition system with the help of local histogram and optimized with FFBNN-AAPSO. In the proposed system, first the input eye images are fetched from the iris database, it is then preprocessed using adaptive median filter to remove noise. Then the features which are extracted from the preprocessed image are given to FFBNN for training the neural network. In order to get accurate results, the FFBNN parameters are optimized using the proposed AAPSO (Adaptive Acceleration Particle Swarm Optimization). In the testing process, the images are preprocessed and subjected to feature extraction process. Then the output obtained from the feature extraction process is given to well trained and optimized FFBNN-AAPSO to check whether the given image is recognized or not. In order to analyze the performance of our proposed recognition system, images from UBRIS iris database are used and the performance of the system is compared with an existing system, PSO-Feed Forward Neural Network and Feed Forward NN.