An Effective CNN based Feature Extraction Approach for Iris Recognition System
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Abstract
Biometrics are playing an important role in security. Biometrics based on computer vision includes facial recognition, fingerprints, and iris to create efficient authentication systems. Iris identification is one of the best methods for providing individuals with unique authentication based on their IRIS structure. In this work, accurate iris recognition is based on pre-processing techniques, segmentation using Circular Hough transform along with canny edge detector, and normalization by daugman’s process. Using Convolutional Neural Networks, the suggested system is trained to extract features of normalized input iris images. This is followed by the Softmax classifier to classify into one out of 224 classes from the IITD iris dataset along with108 classes from the CASIA V1 iris dataset. It can be concluded that the performance of our proposed system is influenced by the choice of hyper parameters and tuning of its deep networks and optimizers. By achieving 98 % and 95.4 % accuracies respectively, it outperforms current methods.
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