# State Of The Art In Open-Set Iris Presentation Attack Detection

arXiv: https://arxiv.org/abs/2208.10564 · HTML: https://mlsanity.com/paper/2208.10564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition](https://mlsanity.com/paper/2009.00749/) (2020)
- [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)
- [Assessment of iris recognition reliability for eyes affected by ocular pathologies](https://mlsanity.com/paper/1809.00206/) (2018)
- [Iris Presentation Attack Detection: Where Are We Now?](https://mlsanity.com/paper/2006.13252/) (2020)
- [Presentation Attack Detection for Cadaver Iris](https://mlsanity.com/paper/1807.04058/) (2018)
- [Robust Iris Presentation Attack Detection Fusing 2D and 3D Information](https://mlsanity.com/paper/2002.09137/) (2020)
- [Open Source Presentation Attack Detection Baseline for Iris Recognition](https://mlsanity.com/paper/1809.10172/) (2018)

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

- [Iris Presentation Attack: Assessing the Impact of Combining Vanadium Dioxide Films with Artificial Eyes](https://mlsanity.com/paper/2311.12773/) (2023)

## Abstract (arXiv)

Research in presentation attack detection (PAD) for iris recognition has largely moved beyond evaluation in "closed-set" scenarios, to emphasize ability to generalize to presentation attack types not present in the training data. This paper offers several contributions to understand and extend the state-of-the-art in open-set iris PAD. First, it describes the most authoritative evaluation to date of iris PAD. We have curated the largest publicly-available image dataset for this problem, drawing from 26 benchmarks previously released by various groups, and adding 150,000 images being released with the journal version of this paper, to create a set of 450,000 images representing authentic iris and seven types of presentation attack instrument (PAI). We formulate a leave-one-PAI-out evaluation protocol, and show that even the best algorithms in the closed-set evaluations exhibit catastrophic failures on multiple attack types in the open-set scenario. This includes algorithms performing well in the most recent LivDet-Iris 2020 competition, which may come from the fact that the LivDet-Iris protocol emphasizes sequestered images rather than unseen attack types. Second, we evaluate the accuracy of five open-source iris presentation attack algorithms available today, one of which is newly-proposed in this paper, and build an ensemble method that beats the winner of the LivDet-Iris 2020 by a substantial margin. This paper demonstrates that closed-set iris PAD, when all PAIs are known during training, is a solved problem, with multiple algorithms showing very high accuracy, while open-set iris PAD, when evaluated correctly, is far from being solved. The newly-created dataset, new open-source algorithms, and evaluation protocol, made publicly available with the journal version of this paper, provide the experimental artifacts that researchers can use to measure progress on this important problem.