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

Attention-Guided Network for Iris Presentation Attack Detection