# Micro Stripes Analyses for Iris Presentation Attack Detection

arXiv: https://arxiv.org/abs/2010.14850 · HTML: https://mlsanity.com/paper/2010.14850/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [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)
- [Few-shot One-class Domain Adaptation Based on Frequency for Iris Presentation Attack Detection](https://mlsanity.com/paper/2204.00376/) (2022)
- [Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains](https://mlsanity.com/paper/2205.02573/) (2022)
- [Single Domain Dynamic Generalization for Iris Presentation Attack Detection](https://mlsanity.com/paper/2305.12800/) (2023)
- [A Systematic Failure Analysis of Vision Foundation Models for Open Set Iris Presentation Attack Detection](https://mlsanity.com/paper/2605.19020/) (2026)

## Abstract (arXiv)

Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segmentation is modified. For our presentation attack detection network to better model the classification problem, the segmented area is processed to provide lower dimensional input segments and a higher number of learning samples. Our proposed Micro Stripes Analyses (MSA) solution samples the segmented areas as individual stripes. Then, the majority vote makes the final classification decision of those micro-stripes. Experiments are demonstrated on five databases, where two databases (IIITD-WVU and Notre Dame) are from the LivDet-2017 Iris competition. An in-depth experimental evaluation of this framework reveals a superior performance compared with state-of-the-art algorithms. Moreover, our solution minimizes the confusion between textured (attack) and soft (bona fide) contact lens presentations.