  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.
