# Iris Presentation Attack Detection: Where Are We Now?

arXiv: https://arxiv.org/abs/2006.13252 · HTML: https://mlsanity.com/paper/2006.13252/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Human-Aided Saliency Maps Improve Generalization of Deep Learning](https://mlsanity.com/paper/2105.03492/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition](https://mlsanity.com/paper/2310.04541/) (2023)
- [Privacy-Safe Iris Presentation Attack Detection](https://mlsanity.com/paper/2408.02750/) (2024)
- [Grains of Saliency: Optimizing Saliency-based Training of Biometric Attack Detection Models](https://mlsanity.com/paper/2405.00650/) (2024)
- [State Of The Art In Open-Set Iris Presentation Attack Detection](https://mlsanity.com/paper/2208.10564/) (2022)
- [Single Domain Dynamic Generalization for Iris Presentation Attack Detection](https://mlsanity.com/paper/2305.12800/) (2023)
- [VRBiom: A New Periocular Dataset for Biometric Applications of HMD](https://mlsanity.com/paper/2407.02150/) (2024)
- [AutoSIGHT: Automatic Eye Tracking-based System for Immediate Grading of Human experTise](https://mlsanity.com/paper/2508.01015/) (2025)
- [Training Better Deep Learning Models Using Human Saliency](https://mlsanity.com/paper/2410.16190/) (2024)
- [VISER: Visually-Informed System for Enhanced Robustness in Iris Presentation Attack Detection](https://mlsanity.com/paper/2603.17859/) (2026)
- [What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection](https://mlsanity.com/paper/2606.13528/) (2026)
- [Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach](https://mlsanity.com/paper/2608.08977/) (2026)

## Abstract (arXiv)

As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount. This work presents an overview of the most important advances in the area of iris presentation attack detection published in recent two years. Newly-released, publicly-available datasets for development and evaluation of iris presentation attack detection are discussed. Recent literature can be seen to be broken into three categories: traditional "hand-crafted" feature extraction and classification, deep learning-based solutions, and hybrid approaches fusing both methodologies. Conclusions of modern approaches underscore the difficulty of this task. Finally, commentary on possible directions for future research is provided.