The use of the iris as a biometric identifier has increased dramatically over the last 30 years, prompting privacy and security concerns about the use of iris images in research. It can be difficult to acquire iris image databases due to ethical concerns, and this can be a barrier for those performing biometrics research. In this paper, we describe and show how to create a database of realistic, biometrically unidentifiable colored iris images by training a diffusion model within an open-source diffusion framework. Not only were we able to verify that our model is capable of creating iris textures that are biometrically unique from the training data, but we were also able to verify that our model output creates a full distribution of realistic iris pigmentations. We highlight the fact that the utility of diffusion networks to achieve these criteria with relative ease, warrants additional research in its use within the context of iris database generation and presentation attack security.
View on arXiv@article{zhang2025_2503.11930,
title={ Generating a Biometrically Unique and Realistic Iris Database },
author={ Jingxuan Zhang and Robert J. Hart and Ziqian Bi and Shiaofen Fang and Susan Walsh },
journal={arXiv preprint arXiv:2503.11930},
year={ 2025 }
}