The knowledge and technologies that used to be limited to laboratories and academia, are becoming increasingly available to everyone. The growing capabilities of personal computers allow for the implementation of systems that recognize unique iris features anywhere, without the need for high-end or expensive equipment. These types of iris recognition systems are being used in environments with even the highest security demands. The United Arab Emirates have been using this kind of person identification for more than a decade, India is using iris recognition as part of its Unique ID program and a number of airport are using biometric identification methods to speed up border security check. Given the wide range of uses and the need for faster, more reliable and secure algorithms, the search for alternative methods is necessary and vital. The goal of this thesis is to research alternative methods of iris recognition and to determine how successful they are compared to conventional methods. As an alternative method we chose computing feature vectors using a pre-trained deep convolutional neural network called GoogLeNet. The input image files used for the experiments were from a freely available image database CASIA V4, published by the Chinese Academy of Sciences. The collection has six different subsets with different attributes or characteristics. For the purpose of the thesis we have chosen only four subsets and ran them through three different procedures. The first is the conventional method computing feature vectors; the second is the hybrid method, in which the input image in a neural network is a segmented and normalized iris image; the third is the direct method in which the input image file in the deep neural network is an unedited iris image. The research result is a deep analysis and comparison of the three presented methods.
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