Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains

TL;DR

This paper introduces a novel attention-based deep pixel-wise binary supervision method for iris presentation attack detection, demonstrating improved generalization across different databases and spectra.

Contribution

The paper proposes an innovative A-PBS approach that captures fine-grained features and uses attention mechanisms to enhance iris PAD performance across diverse conditions.

Findings

A-PBS outperforms existing methods in cross-database tests.

The approach demonstrates robustness in intra- and cross-spectrum scenarios.

Extensive experiments validate the method's generalizability.

Abstract

Iris Presentation Attack Detection (PAD) is essential to secure iris recognition systems. Recent iris PAD solutions achieved good performance by leveraging deep learning techniques. However, most results were reported under intra-database scenarios and it is unclear if such solutions can generalize well across databases and capture spectra. These PAD methods run the risk of overfitting because of the binary label supervision during the network training, which serves global information learning but weakens the capture of local discriminative features. This chapter presents a novel attention-based deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise supervision to capture the fine-grained pixel/patch-level cues and attention mechanism to guide the network to automatically find regions where most contribute to an accurate PAD decision. Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods. We additionally conduct extensive experiments under intra-/cross-database and intra-/cross-spectrum for detailed analysis. The results of our experiments indicates the generalizability of the A-PBS iris PAD approach.