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

• URL: http://arxiv.org/abs/2605.20735v1
• Date: Wed, 20 May 2026 05:39:34 GMT
• Title: Lowering the Barrier to IREX Participation: Open-Source Algorithms, Toolkit, and Benchmarking for Iris Recognition
• Abstract summary: This paper proposes two new open-source iris recognition algorithms, providing both Python and IREX-compliant C++ implementations.<n>The new methods consist of two Neural Networks trained with: (i) Triplet loss with Batch-Hard Triplet mining (TripletIris), and (ii) ArcFace loss (ArcIris)<n>The paper also provides open-source models for iris segmentation and circle estimation that can be incorporated into any new iris recognition method.
• Score: 8.456537372135163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes two new open-source iris recognition algorithms, providing both Python and IREX-compliant C++ implementations to be submitted to the official IREX X program. This work has two primary goals: (a) to conduct the first-ever assessment of open-source iris recognition solutions according to IREX testing protocols, and (b) to offer a model C++ submission that significantly facilitates the entry of other teams' open-source methods into the IREX evaluation. The new methods consist of two Neural Networks trained with: (i) Triplet loss with Batch-Hard Triplet mining (TripletIris), and (ii) ArcFace loss (ArcIris). The paper also provides open-source IREX-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). Except for CRYPTS, which faced timing constraints during 1:N search, these methods have undergone the official IREX X evaluation and have also been assessed using several popular academic benchmarks: Quality-Face/Iris Research Ensemble, 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. Finally, this paper also provides open-source models for iris segmentation and circle estimation that can be incorporated into any new iris recognition method.

Related papers

• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.<n>These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.<n>We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation [54.707460684650584]
  Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG) RAGLAB is a modular and research-oriented open-source library that reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms.
  arXiv  Detail & Related papers  (2024-08-21T07:20:48Z)
• KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection [48.66703222700795]
  We resort to a novel kernel strategy to identify the most informative point clouds to acquire labels. To accommodate both one-stage (i.e., SECOND) and two-stage detectors, we incorporate the classification entropy tangent and well trade-off between detection performance and the total number of bounding boxes selected for annotation. Our results show that approximately 44% box-level annotation costs and 26% computational time are reduced compared to the state-of-the-art method.
  arXiv  Detail & Related papers  (2023-07-16T04:27:03Z)
• Open-Set Automatic Target Recognition [52.27048031302509]
  Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution. We propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms.
  arXiv  Detail & Related papers  (2022-11-10T21:28:24Z)
• State Of The Art In Open-Set Iris Presentation Attack Detection [4.843820624525483]
  This paper offers contributions to understand and extend the state-of-the-art in open-set iris presentation attack detection (PAD) We have curated the largest publicly-available image dataset for this problem, drawing from 26 benchmarks previously released by various groups. We show that even the best algorithms in the closed-set evaluations exhibit catastrophic failures on multiple attack types in the open-set scenario.
  arXiv  Detail & Related papers  (2022-08-22T19:40:59Z)
• Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning [96.72850130126294]
  We propose an Iris U-transformer (IrisUsformer) for accurate and reliable iris segmentation. For better accuracy, we elaborately design IrisUsformer by adopting position-sensitive operation and re-packaging transformer block. We show that IrisUsformer achieves better segmentation accuracy using 35% MACs of the SOTA IrisParseNet.
  arXiv  Detail & Related papers  (2021-10-20T01:37:19Z)
• Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings [63.90855798947425]
  Presentation attacks pose major challenges to most of the biometric modalities. We propose a generalized deep learning-based presentation attack detection network, MVANet. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
  arXiv  Detail & Related papers  (2020-10-25T22:42:27Z)
• On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application [16.382021536377437]
  We evaluate a set of iris recognition algorithms suitable for Head-Mounted Displays (HMD) We employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD.
  arXiv  Detail & Related papers  (2020-10-20T17:05:11Z)
• Open Source Iris Recognition Hardware and Software with Presentation Attack Detection [10.579257329579676]
  This paper proposes the first known to us open source hardware and software iris recognition system with presentation attack detection (PAD) It can be easily assembled for about 75 USD using Raspberry Pi board and a few peripherals. The proposed iris recognition runs in about 3.2 seconds and the proposed PAD runs in about 4.5 seconds on Raspberry Pi 3B+.
  arXiv  Detail & Related papers  (2020-08-19T02:02:16Z)
• Robust Iris Presentation Attack Detection Fusing 2D and 3D Information [15.97343723521826]
  This paper proposes a method that combines two-dimensional and three-dimensional properties of the observed iris. The 2D (textural) iris features are extracted by a state-of-the-art method employing Binary Statistical Image Features (BSIF) The 3D (shape) iris features are reconstructed by a photometric stereo method from only two images captured under near-infrared illumination placed at two different angles.
  arXiv  Detail & Related papers  (2020-02-21T05:44:38Z)
• Are Gabor Kernels Optimal for Iris Recognition? [4.658023970671232]
  Gabor kernels are widely accepted as dominant filters for iris recognition. We learn data-driven kernels that can be easily transplanted into open-source iris recognition software.
  arXiv  Detail & Related papers  (2020-02-20T17:51:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.