# Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition

arXiv: https://arxiv.org/abs/2208.03138 · HTML: https://mlsanity.com/paper/2208.03138/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 3 Aug 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Image Provenance Analysis at Scale](https://mlsanity.com/paper/1801.06510/) (2018)
- [Iris Recognition After Death](https://mlsanity.com/paper/1804.01962/) (2018)
- [Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification](https://mlsanity.com/paper/1807.05245/) (2018)
- [Iris Recognition Based on SIFT Features](https://mlsanity.com/paper/2111.00176/) (2021)
- [An Experimental Study of Deep Convolutional Features For Iris Recognition](https://mlsanity.com/paper/1702.01334/) (2017)
- [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)
- [Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition](https://mlsanity.com/paper/2112.00849/) (2021)
- [Post-mortem Human Iris Recognition](https://mlsanity.com/paper/1809.00208/) (2018)
- [Are Gabor Kernels Optimal for Iris Recognition?](https://mlsanity.com/paper/2002.08959/) (2020)

## Built on by (most cited first, 11 of 11)

- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training](https://mlsanity.com/paper/2306.05527/) (2023)
- [Forensic Iris Image Synthesis](https://mlsanity.com/paper/2312.04125/) (2023)
- [NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios](https://mlsanity.com/paper/2309.09518/) (2023)
- [MENTOR: Human Perception-Guided Pretraining for Increased Generalization](https://mlsanity.com/paper/2310.19545/) (2023)
- [Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligence](https://mlsanity.com/paper/2402.08466/) (2024)
- [Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency](https://mlsanity.com/paper/2410.16115/) (2024)
- [Lowering the Barrier to IREX Participation: Open-Source Algorithms, Toolkit, and Benchmarking for Iris Recognition](https://mlsanity.com/paper/2605.20735/) (2026)
- [Training Better Deep Learning Models Using Human Saliency](https://mlsanity.com/paper/2410.16190/) (2024)
- [Psych-Occlusion: Using Visual Psychophysics for Aerial Detection of Occluded Persons during Search and Rescue](https://mlsanity.com/paper/2412.05553/) (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)

Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons. As a machine learning-based technique in a predominantly human-controlled task, forensic recognition serves as "back-up" to human expertise in the task of post-mortem identification. As such, the machine learning model must be (a) interpretable, and (b) post-mortem-specific, to account for changes in decaying eye tissue. In this work, we propose a method that satisfies both requirements, and that approaches the creation of a post-mortem-specific feature extractor in a novel way employing human perception. We first train a deep learning-based feature detector on post-mortem iris images, using annotations of image regions highlighted by humans as salient for their decision making. In effect, the method learns interpretable features directly from humans, rather than purely data-driven features. Second, regional iris codes (again, with human-driven filtering kernels) are used to pair detected iris patches, which are translated into pairwise, patch-based comparison scores. In this way, our method presents human examiners with human-understandable visual cues in order to justify the identification decision and corresponding confidence score. When tested on a dataset of post-mortem iris images collected from 259 deceased subjects, the proposed method places among the three best iris matchers, demonstrating better results than the commercial (non-human-interpretable) VeriEye approach. We propose a unique post-mortem iris recognition method trained with human saliency to give fully-interpretable comparison outcomes for use in the context of forensic examination, achieving state-of-the-art recognition performance.