# Lowering the Barrier to IREX Participation: Open-Source Algorithms, Toolkit, and Benchmarking for Iris Recognition

arXiv: https://arxiv.org/abs/2605.20735 · HTML: https://mlsanity.com/paper/2605.20735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Parallelizing the dual revised simplex method](https://mlsanity.com/paper/1503.01889/) (2015)
- [OpenEDS: Open Eye Dataset](https://mlsanity.com/paper/1905.03702/) (2019)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition](https://mlsanity.com/paper/2009.00749/) (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)
- [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)
- [Robust Iris Presentation Attack Detection Fusing 2D and 3D Information](https://mlsanity.com/paper/2002.09137/) (2020)
- [See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation](https://mlsanity.com/paper/1908.09443/) (2019)
- [Near-infrared and visible-light periocular recognition with Gabor features using frequency-adaptive automatic eye detection](https://mlsanity.com/paper/2211.05544/) (2022)
- [Implications of Ocular Pathologies for Iris Recognition Reliability](https://mlsanity.com/paper/1809.00168/) (2018)
- [Two-headed eye-segmentation approach for biometric identification](https://mlsanity.com/paper/2209.15471/) (2022)
- [Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions](https://mlsanity.com/paper/2307.14617/) (2023)
- [Iris Recognition for Infants](https://mlsanity.com/paper/2501.01375/) (2025)

## Built on by (most cited first, 1 of 1)

- [Ocular Verification for Virtual Reality](https://mlsanity.com/paper/2607.20790/) (2026)

## Abstract (arXiv)

NIST Iris Exchange (IREX) offers an appealing solution to evaluating new open-source iris recognition algorithms, but it presents high barriers to entry because these algorithms must be written in C++, using a specific API, and adapted to meet strict IREX speed and memory constraints. The main goal of this paper is to lower these barriers and advance open-source iris recognition large-scale evaluations by offering: (a) two new modern deep learning-based open-source iris matchers (ArcIris and TripletIris), along with their C++ IREX X-compliant implementations, which are the first open-source iris recognition methods included into the IREX X leaderboard (and thus IREX-vetted), as well as new segmentation and iris circular approximation models that can be incorporated into any new iris recognition method, and (b) a performance assessment (according to IREX X testing protocols) of all major and currently available open-source iris recognition solutions. The paper also provides Python implementations of the new ArcIris and TripletIris methods and discusses the differences one may encounter between C++ and Python implementations of the same conceptually equivalent approaches. Finally, the paper offers open-source, IREX X-compliant C++ implementations of two existing methods: (a) an iris image filtering-based algorithm utilizing human saliency-driven kernels (HDBIF), and (b) a human-interpretable algorithm for detecting and comparing Fuchs' crypts (CRYPTS). In addition to IREX X evaluation results, the paper reports the performance of all methods on major academic benchmarks: Quality-Face/Iris Research Ensemble (Q-FIRE), Warsaw-Biobase Post-Mortem Iris, CASIA-Iris-Thousand-V4, CASIA-Iris-Lamp-V4, IIT Delhi Iris Database, IIITD Contact Lens Iris Database, NDIris3D, and Notre Dame Variable Iris Image Quality Release 2 (VII-Q-R2).