Aymen AbdElKader CHERIF, Mohammed Salim BENSALEM2023-09-202023-09-202023-09-20EL/2023https://repository.univ-msila.dz/handle/123456789/40879Iris recognition refers to the automated process of recognising individuals based on their iris patterns. The seemingly stochastic nature of the iris stroma makes it a distinctive cue for biometric recognition. This textural descriptor has been observed to be a robust feature descriptor with very low false match rates and low computational complexity. However, recent advancements in deep learning and computer vision indicate that generic descriptors extracted using Convolutional Neural Networks (CNNs) are able to represent complex image characteristics. Deep CNN is a powerful visual model of machine learning. We tend to present robustness and an effective structure for the iris recognition system. The image first pass through these stages: enhancing the image quality, determining the iris and pupil centre and radius for iris segmentation, and converting the image from the Cartesian coordinates to the polar coordinates to reduce the time of processing. The proposed system is named IRISNet which extracts the feature and classifies them automatically without any domain knowledge. The architecture of IRISNet consists of CNN layers to extract features and a softmax layer to classify them into N classes for training CNN, the back-propagation algorithm and Adam optimisation method are used for updating the weights and the learning rate, respectively. The performance of the proposed system was evaluated using the Sdumla iris database. The results obtained from the proposed system outperform the supervised classification model (VGG16, MobileNet, Inception, and Xception). The identification rate is 97.32% and 96.43% for original and normalised images, respectively. The recognition time per person is less than 1s. Experimental results conclude that the proposed work obtained good performance compared to existing methodsfrIris, CNN, Training, Convolution, Deep Learning, Image Recognition, Testing, Feature Extraction.Thesis