The thesis deals with the approach of iris recognition using neural networks. The idea is to correctly detect the iris region from the image of the eye, from which, using suitable algorithms and methods, we then obtain the so-called feature vector. The feature vector represents a compact and unique description of each image, which is then passed to different neural networks. For the neural networks, we use classical neural networks, which are given feature vectors as input. In the end, we also test the convolutional neural networks where the original image is given as input. For classical neural networks, we tested a large number of combinations of image enhancement methods, feature extraction methods and neural networks. Pattern recognition network, in combination with Gabor filters, has been shown to achieve the best accuracy of 95.7 percent. Meanwhile, for convolutional neural networks, the ResNet50 network performed best with an accuracy of 96.4 percent.
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