论文标题
虹膜表现攻击检测的注意力引导网络
Attention-Guided Network for Iris Presentation Attack Detection
论文作者
论文摘要
卷积神经网络（CNN）越来越多地用于解决虹膜表现攻击检测的问题。在这项工作中，我们提出了注意力引导的虹膜表现攻击检测（AG-PAD），以增强CNN具有注意机制。两种类型的注意力模块独立附加在骨干网络的最后一个卷积层之上。具体而言，通道注意模块用于建模特征之间的通道间关系，而位置注意模块用于模拟特征之间的空间间关系。使用元素的总和来融合这两个注意模块。此外，引入了一种新型的分层注意机制。涉及JHU-APL专有数据集和基准Livdet-iris-2017数据集的实验表明，所提出的方法可实现有希望的结果。据我们所知，这是利用虹膜表现攻击检测中注意力机制使用的第一项工作。
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.
