  Iris presentation attack detection (PAD) plays a vital role in iris
recognition systems. Most existing CNN-based iris PAD solutions 1) perform only
binary label supervision during the training of CNNs, serving global
information learning but weakening the capture of local discriminative
features, 2) prefer the stacked deeper convolutions or expert-designed
networks, raising the risk of overfitting, 3) fuse multiple PAD systems or
various types of features, increasing difficulty for deployment on mobile
devices. Hence, we propose a novel attention-based deep pixel-wise binary
supervision (A-PBS) method. Pixel-wise supervision is first able to capture the
fine-grained pixel/patch-level cues. Then, the attention mechanism guides the
network to automatically find regions that most contribute to an accurate PAD
decision. Extensive experiments are performed on LivDet-Iris 2017 and three
other publicly available databases to show the effectiveness and robustness of
proposed A-PBS methods. For instance, the A-PBS model achieves an HTER of 6.50%
on the IIITD-WVU database outperforming state-of-the-art methods.
