# Forensic Iris Image Synthesis

arXiv: https://arxiv.org/abs/2312.04125 · HTML: https://mlsanity.com/paper/2312.04125/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition](https://mlsanity.com/paper/1807.05248/) (2018)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection](https://mlsanity.com/paper/2012.02374/) (2020)
- [Iris Recognition After Death](https://mlsanity.com/paper/1804.01962/) (2018)
- [Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition](https://mlsanity.com/paper/2208.03138/) (2022)
- [Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN](https://mlsanity.com/paper/1812.04822/) (2018)
- [Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition](https://mlsanity.com/paper/2112.00849/) (2021)
- [Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images](https://mlsanity.com/paper/2107.12014/) (2021)

## Built on by (most cited first, 5 of 5)

- [Privacy-Safe Iris Presentation Attack Detection](https://mlsanity.com/paper/2408.02750/) (2024)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [Generating a Biometrically Unique and Realistic Iris Database](https://mlsanity.com/paper/2503.11930/) (2025)
- [Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies](https://mlsanity.com/paper/2506.02626/) (2025)
- [Forensic Iris Image-Based Post-Mortem Interval Estimation](https://mlsanity.com/paper/2404.10172/) (2024)

## Abstract (arXiv)

Post-mortem iris recognition is an emerging application of iris-based human identification in a forensic setup, able to correctly identify deceased subjects even three weeks post-mortem. This technique thus is considered as an important component of future forensic toolkits. The current advancements in this field are seriously slowed down by exceptionally difficult data collection, which can happen in mortuary conditions, at crime scenes, or in ``body farm'' facilities. This paper makes a novel contribution to facilitate progress in post-mortem iris recognition by offering a conditional StyleGAN-based iris synthesis model, trained on the largest-available dataset of post-mortem iris samples acquired from more than 350 subjects, generating -- through appropriate exploration of StyleGAN latent space -- multiple within-class (same identity) and between-class (different new identities) post-mortem iris images, compliant with ISO/IEC 29794-6, and with decomposition deformations controlled by the requested PMI (post mortem interval). Besides an obvious application to enhance the existing, very sparse, post-mortem iris datasets to advance -- among others -- iris presentation attack endeavors, we anticipate it may be useful to generate samples that would expose professional forensic human examiners to never-seen-before deformations for various PMIs, increasing their training effectiveness. The source codes and model weights are made available with the paper.