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

arXiv: https://arxiv.org/abs/1812.04822 · HTML: https://mlsanity.com/paper/1812.04822/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition](https://mlsanity.com/paper/2108.01569/) (2021)
- [Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises](https://mlsanity.com/paper/2211.05629/) (2022)
- [Privacy-Safe Iris Presentation Attack Detection](https://mlsanity.com/paper/2408.02750/) (2024)
- [Forensic Iris Image Synthesis](https://mlsanity.com/paper/2312.04125/) (2023)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images](https://mlsanity.com/paper/2107.12014/) (2021)
- [Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies](https://mlsanity.com/paper/2506.02626/) (2025)
- [Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts](https://mlsanity.com/paper/2308.10704/) (2023)
- [Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations](https://mlsanity.com/paper/2511.09749/) (2025)

## Abstract (arXiv)

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.