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

Abstract

Generating iris images which look realistic is both an interesting and challenging problem. Most of the classical statistical models are not powerful enough to capture the complicated texture representation in iris images, and therefore fail to generate iris images which look realistic. In this work, we present 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 popular iris databases, and generate images which look very realistic, and similar to the image distribution in those databases. Through experimental results, we show that the generated iris images have a good diversity, and are able to capture different part of the prior distribution.

References in corpus (5)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Multicolumn Networks for Face Recognition
• Iris Recognition Using Scattering Transform and Textural Features

Cited by in corpus (4)

• Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN
• Palm-GAN: Generating Realistic Palmprint Images Using Total-Variation Regularized GAN
• Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition
• Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images