الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Iris is a common biometric used for identity verification. Iris identification is one of the best ways to give people individual authentication based on their iris anatomy. This Thesis main objective is to evaluate how well deep learning networks perform on iris image datasets. The image goes through the following stages: improving image quality; employing the Hough transform and the integro-differential operator to segment the iris; and reducing processing time by changing the image’s 150x300 dimensions from Cartesian to polar coordinates. Transfer learning is used to implement the iris classification in three deep learning networks: VGG19, InceptionV3, and Iris Net. The study presents several parameters, including the accuracy of each deep learning network, that were used to create an effective automated iris recognition model. For the iris recognition challenge, the study also compares the system’s identification ability with several CNN models to determine the optimum outcome. The proposed iris recognition system is tested using Utiris-V1, CASIA Iris Twins-V3, and CASIA-Iris-V3 Interval. The system produced excellent outcomes with a high accuracy rate. Results of the proposed system shows that Vgg-19 performs best, with an overall database accuracy of 100% and a per-person recognition time of less than one second