# Forensic Iris Image-Based Post-Mortem Interval Estimation

arXiv: https://arxiv.org/abs/2404.10172 · HTML: https://mlsanity.com/paper/2404.10172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Iris Recognition After Death](https://mlsanity.com/paper/1804.01962/) (2018)
- [Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes](https://mlsanity.com/paper/1912.02512/) (2019)
- [Forensic Iris Image Synthesis](https://mlsanity.com/paper/2312.04125/) (2023)

## Abstract (arXiv)

Post-mortem iris recognition is an emerging application of iris-based human identification in a forensic setup. One factor that may be useful in conditioning iris recognition methods is the tissue decomposition level, which is correlated with the post-mortem interval (PMI), ie the number of hours that have elapsed since death. PMI, however, is not always available, and its precise estimation remains one of the core challenges in forensic examination. This paper presents the first known to us method of the PMI estimation directly from iris images captured after death. To assess the feasibility of the iris-based PMI estimation, we designed models predicting the PMI from (a) near-infrared (NIR), (b) visible (RGB), and (c) multispectral (RGB+NIR) forensic iris images. Models were evaluated following a 10-fold cross-validation, in (S1) sample-disjoint, (S2) subject-disjoint, and (S3) cross-dataset scenarios. We explore two data balancing techniques for S3: resampling-based balancing (S3-real), and synthetic data-supplemented balancing (S3-synthetic). We found that using the multispectral data offers a spectacularly low mean absolute error (MAE) of ≈ 3.5 hours in the scenario (S1), a bit worse MAE ≈ 17.5 hours in the scenario (S2), and MAE ≈ 45.77 hours in the scenario (S3). Additionally, supplementing the training set with synthetically-generated forensic iris images (S3-synthetic) significantly enhances the models' ability to generalize to new NIR, RGB and multispectral data collected in a different lab. This suggests that if the environmental conditions are favorable (eg, bodies are kept in low temperatures), forensic iris images provide features that are indicative of the PMI and can be automatically estimated.