# Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

arXiv: https://arxiv.org/abs/1811.10068 · HTML: https://mlsanity.com/paper/1811.10068/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

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

- [Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network](https://mlsanity.com/paper/2106.14845/) (2021)
- [Iris Liveness Detection using a Cascade of Dedicated Deep Learning Networks](https://mlsanity.com/paper/2105.14009/) (2021)
- [A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2412.07199/) (2024)
- [EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images](https://mlsanity.com/paper/2112.11610/) (2021)
- [Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains](https://mlsanity.com/paper/2205.02573/) (2022)
- [DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2111.00919/) (2021)

## Abstract (arXiv)

The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts). This work presents a new approach in iris Presentation Attack Detection (PAD), by exploring combinations of Convolutional Neural Networks (CNNs) and transformed input spaces through binarized statistical image features (BSIF). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand.An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris2017 competition both for intra- and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.