An Explainable Attention-Guided Iris Presentation Attack Detector
Cunjian Chen, Arun Ross
Abstract
Convolutional Neural Networks (CNNs) are being increasingly used to address the problem of iris presentation attack detection. In this work, we propose an explainable attention-guided iris presentation attack detector (AG-PAD) to augment CNNs with attention mechanisms and to provide visual explanations of model predictions. Two types of attention modules are independently placed 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 detection results while explaining occurrences of salient regions for discriminative feature learning. To the best of our knowledge, this is the first work that exploits the use of attention mechanisms in iris presentation attack detection.