# Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

arXiv: https://arxiv.org/abs/2003.03151 · HTML: https://mlsanity.com/paper/2003.03151/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network](https://mlsanity.com/paper/2106.14845/) (2021)
- [Fairness in Face Presentation Attack Detection](https://mlsanity.com/paper/2209.09035/) (2022)
- [Bias and Diversity in Synthetic-based Face Recognition](https://mlsanity.com/paper/2311.03970/) (2023)
- [Towards Explaining Demographic Bias through the Eyes of Face Recognition Models](https://mlsanity.com/paper/2208.13400/) (2022)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [There is an elephant in the room: Towards a critique on the use of fairness in biometrics](https://mlsanity.com/paper/2112.11193/) (2021)
- [Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains](https://mlsanity.com/paper/2205.02573/) (2022)
- [Race Bias Analysis of Bona Fide Errors in face anti-spoofing](https://mlsanity.com/paper/2210.05366/) (2022)
- [Security and Privacy Product Inclusion](https://mlsanity.com/paper/2404.13220/) (2024)

## Abstract (arXiv)

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD-2013 database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.