Attention-Guided Network for Iris Presentation Attack Detection

TL;DR

This paper introduces an attention-guided CNN framework for iris presentation attack detection, employing channel and position attention modules with a hierarchical mechanism, achieving promising results on proprietary and benchmark datasets.

Contribution

It is the first to incorporate attention mechanisms into iris presentation attack detection, enhancing CNN focus on relevant features.

Findings

Achieves improved detection accuracy on iris attack datasets.

Demonstrates the effectiveness of attention modules in iris PAD.

First to apply hierarchical attention in this domain.

Abstract

Convolutional Neural Networks (CNNs) are being increasingly used to address the problem of iris presentation attack detection. In this work, we propose attention-guided iris presentation attack detection (AG-PAD) to augment CNNs with attention mechanisms. Two types of attention modules are independently appended on top of the last convolutional layer of the backbone network. Specifically, the channel attention module is used to model the inter-channel relationship between features, while the position attention module is used to model inter-spatial relationship between features. An element-wise sum is employed to fuse these two attention modules. Further, a novel hierarchical attention mechanism is introduced. Experiments involving both a JHU-APL proprietary dataset and the benchmark LivDet-Iris-2017 dataset suggest that the proposed method achieves promising results. To the best of our knowledge, this is the first work that exploits the use of attention mechanisms in iris presentation attack detection.