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Generating iris images which look realistic is both an interesting andchallenging problem. Most of the classical statistical models are not powerfulenough to capture the complicated texture representation in iris images, andtherefore fail to generate iris images which look realistic. In this work, wepresent a machine learning framework based on generative adversarial network(GAN), which is able to generate iris images sampled from a prior distribution(learned from a set of training images). We apply this framework to two populariris databases, and generate images which look very realistic, and similar tothe image distribution in those databases. Through experimental results, weshow that the generated iris images have a good diversity, and are able tocapture different part of the prior distribution.

Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN