# Deep Learning for Iris Recognition: A Survey

arXiv: https://arxiv.org/abs/2210.05866 · HTML: https://mlsanity.com/paper/2210.05866/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 210 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 30 of 67)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [ParseNet: Looking Wider to See Better](https://mlsanity.com/paper/1506.04579/) (2015)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation](https://mlsanity.com/paper/1912.07863/) (2019)
- [This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models](https://mlsanity.com/paper/2101.05084/) (2020)
- [CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning](https://mlsanity.com/paper/2112.00686/) (2021)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [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)
- [Human-Aided Saliency Maps Improve Generalization of Deep Learning](https://mlsanity.com/paper/2105.03492/) (2021)
- [D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector](https://mlsanity.com/paper/2007.01381/) (2020)
- [Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art](https://mlsanity.com/paper/1804.00194/) (2018)
- [Deep Representations for Iris, Face, and Fingerprint Spoofing Detection](https://mlsanity.com/paper/1410.1980/) (2014)
- [A Survey on Periocular Biometrics Research](https://mlsanity.com/paper/1810.03360/) (2018)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection](https://mlsanity.com/paper/2012.02374/) (2020)
- [Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network](https://mlsanity.com/paper/2106.14845/) (2021)
- [Iris Presentation Attack Detection: Where Are We Now?](https://mlsanity.com/paper/2006.13252/) (2020)
- [A mathematical motivation for complex-valued convolutional networks](https://mlsanity.com/paper/1503.03438/) (2015)

## Built on by (most cited first, 25 of 25)

- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Towards Iris Presentation Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.06312/) (2025)
- [Deep Learning in Palmprint Recognition-A Comprehensive Survey](https://mlsanity.com/paper/2501.01166/) (2025)
- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations](https://mlsanity.com/paper/2307.05128/) (2023)
- [Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition](https://mlsanity.com/paper/2407.19472/) (2024)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions](https://mlsanity.com/paper/2302.06529/) (2023)
- [Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris Recognition Systems](https://mlsanity.com/paper/2403.12047/) (2023)
- [VRBiom: A New Periocular Dataset for Biometric Applications of HMD](https://mlsanity.com/paper/2407.02150/) (2024)
- [Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer](https://mlsanity.com/paper/2503.04707/) (2025)
- [DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing](https://mlsanity.com/paper/2507.17158/) (2025)
- [ImmerIris: A Large-Scale Dataset and Benchmark for Off-Axis and Unconstrained Iris Recognition in Immersive Applications](https://mlsanity.com/paper/2510.10113/) (2025)
- [SpectraIrisPAD: Leveraging Vision Foundation Models for Spectrally Conditioned Multispectral Iris Presentation Attack Detection](https://mlsanity.com/paper/2512.06103/) (2025)
- [Ethnicity and Biometric Uniqueness: Iris Pattern Individuality in a West African Database](https://mlsanity.com/paper/2309.06521/) (2023)
- [Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement](https://mlsanity.com/paper/2408.10823/) (2024)
- [A Siamese Network to Detect If Two Iris Images Are Monozygotic](https://mlsanity.com/paper/2503.09749/) (2025)
- [An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System](https://mlsanity.com/paper/2507.12873/) (2025)
- [A comprehensive Persian offline handwritten database for investigating the effects of heritability and family relationships on handwriting](https://mlsanity.com/paper/2509.03510/) (2025)
- [Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification](https://mlsanity.com/paper/2510.08160/) (2025)
- [Generalist Multimodal LLMs Gain Biometric Expertise via Human Salience](https://mlsanity.com/paper/2603.17173/) (2026)
- [VISER: Visually-Informed System for Enhanced Robustness in Iris Presentation Attack Detection](https://mlsanity.com/paper/2603.17859/) (2026)
- [A Systematic Failure Analysis of Vision Foundation Models for Open Set Iris Presentation Attack Detection](https://mlsanity.com/paper/2605.19020/) (2026)
- [Towards Robust Iris Recognition Through Occlusion Identification and Conditional Diffusion-Based Reconstruction](https://mlsanity.com/paper/2607.21545/) (2026)
- [Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach](https://mlsanity.com/paper/2608.08977/) (2026)

## Abstract (arXiv)

In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition.