# Beyond Mortality: Advancements in Post-Mortem Iris Recognition through Data Collection and Computer-Aided Forensic Examination

arXiv: https://arxiv.org/abs/2603.26976 · HTML: https://mlsanity.com/paper/2603.26976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling](https://mlsanity.com/paper/1505.07293/) (2015)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition](https://mlsanity.com/paper/2009.00749/) (2020)
- [D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector](https://mlsanity.com/paper/2007.01381/) (2020)
- [Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition](https://mlsanity.com/paper/1807.05248/) (2018)
- [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)
- [Presentation Attack Detection for Cadaver Iris](https://mlsanity.com/paper/1807.04058/) (2018)
- [Gender-From-Iris or Gender-From-Mascara?](https://mlsanity.com/paper/1702.01304/) (2017)
- [Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes](https://mlsanity.com/paper/1912.02512/) (2019)
- [Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition](https://mlsanity.com/paper/2112.00849/) (2021)
- [Human Iris Recognition in Post-mortem Subjects: Study and Database](https://mlsanity.com/paper/1809.00174/) (2018)
- [Post-mortem Human Iris Recognition](https://mlsanity.com/paper/1809.00208/) (2018)
- [Data-Driven Segmentation of Post-mortem Iris Images](https://mlsanity.com/paper/1807.04154/) (2018)
- [Post-mortem Iris Decomposition and its Dynamics in Morgue Conditions](https://mlsanity.com/paper/1911.02837/) (2019)

## Abstract (arXiv)

Post-mortem iris recognition brings both hope to the forensic community (a short-term but accurate and fast means of verifying identity) as well as concerns to society (its potential illicit use in post-mortem impersonation). These hopes and concerns have grown along with the volume of research in post-mortem iris recognition. Barriers to further progress in post-mortem iris recognition include the difficult nature of data collection, and the resulting small number of approaches designed specifically for comparing iris images of deceased subjects. This paper makes several unique contributions to mitigate these barriers. First, we have collected and we offer a new dataset of NIR (compliant with ISO/IEC 19794-6 where possible) and visible-light iris images collected after demise from 259 subjects, with the largest PMI (post-mortem interval) being 1,674 hours. For one subject, the data has been collected before and after death, the first such case ever published. Second, the collected dataset was combined with publicly-available post-mortem samples to assess the current state of the art in automatic forensic iris recognition with five iris recognition methods and data originating from 338 deceased subjects. These experiments include analyses of how selected demographic factors influence recognition performance. Thirdly, this study implements a model for detecting post-mortem iris images, which can be considered as presentation attacks. Finally, we offer an open-source forensic tool integrating three post-mortem iris recognition methods with explainability elements added to make the comparison process more human-interpretable.