CVDec 7, 2023

Forensic Iris Image Synthesis

arXiv:2312.04125v15 citationsh-index: 272024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Originality Synthesis-oriented
AI Analysis

This work addresses a critical data scarcity issue in forensic identification, enabling enhanced datasets for iris recognition and training of forensic examiners, though it is incremental as it applies an existing generative method to a specific domain.

The paper tackles the problem of limited data for post-mortem iris recognition by introducing a conditional StyleGAN-based model that synthesizes realistic iris images with controlled decomposition deformations based on post-mortem interval, generating multiple within-class and between-class images from a dataset of over 350 subjects.

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.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes