# Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition

arXiv: https://arxiv.org/abs/2112.00849 · HTML: https://mlsanity.com/paper/2112.00849/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Adapting Grad-CAM for Embedding Networks](https://mlsanity.com/paper/2001.06538/) (2020)
- [Iris Recognition After Death](https://mlsanity.com/paper/1804.01962/) (2018)
- [An Experimental Study of Deep Convolutional Features For Iris Recognition](https://mlsanity.com/paper/1702.01334/) (2017)
- [Presentation Attack Detection for Cadaver Iris](https://mlsanity.com/paper/1807.04058/) (2018)
- [Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?](https://mlsanity.com/paper/2002.08916/) (2020)
- [ThirdEye: Triplet Based Iris Recognition without Normalization](https://mlsanity.com/paper/1907.06147/) (2019)
- [Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes](https://mlsanity.com/paper/1912.02512/) (2019)
- [Post-mortem Human Iris Recognition](https://mlsanity.com/paper/1809.00208/) (2018)
- [Linear regression analysis of template aging in iris biometrics](https://mlsanity.com/paper/1809.00170/) (2018)
- [Are Gabor Kernels Optimal for Iris Recognition?](https://mlsanity.com/paper/2002.08959/) (2020)

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition](https://mlsanity.com/paper/2208.03138/) (2022)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Forensic Iris Image Synthesis](https://mlsanity.com/paper/2312.04125/) (2023)
- [Post-Mortem Human Iris Segmentation Analysis with Deep Learning](https://mlsanity.com/paper/2408.03448/) (2024)
- [Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement](https://mlsanity.com/paper/2408.10823/) (2024)
- [Beyond Mortality: Advancements in Post-Mortem Iris Recognition through Data Collection and Computer-Aided Forensic Examination](https://mlsanity.com/paper/2603.26976/) (2026)

## Abstract (arXiv)

Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged recently. In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts. The proposed postmortem iris segmentation approach outperforms the state of the art and in addition to iris annulus, as in case of classical iris segmentation methods - detects abnormal regions caused by eye decomposition processes, such as furrows or irregular specular highlights present on the drying and wrinkling cornea. The method was trained and validated with data acquired from 171 cadavers, kept in mortuary conditions, and tested on subject-disjoint data acquired from 259 deceased subjects. To our knowledge, this is the largest corpus of data used in postmortem iris recognition research to date. The source code of the proposed method are offered with the paper. The test data will be available through the National Archive of Criminal Justice Data (NACJD) archives.