An Iris Recognition System Using Deep convolutional Neural Network

Abstract

Abstract Machine learning rises in varied areas of computer science. A deep conventional neural network is powerful visual models of machine learning. We tend to present robustness and effective structure for the iris recognition system. The image first pass through these stages: enhancing the image quality, determine the iris and pupil center and radius for iris segmentation, converting the image from the Cartesian coordinates to the polar coordinates to reduce the time of processing. The proposed system is named IRISNet that extracting the feature and classifying them automatically without any domain knowledge. The architecture of IRISNet consists of Convolutional Neural Network layers for extract feature and Softmax layer to classify these features into N classes, for training CNN the backpropagation algorithm and Adam optimization method are used for updating the weights and learning rate, respectively. The performance of the proposed system was evaluated using IITD V1 iris database. The results obtained from the proposed system outperform supervised classification model (SVM, KNN, DT, and NB). The identification rate 97.32% and 96.43% for original and normalized images respectively. The recognition time per person is less than one second.