  Iris Presentation Attack Detection (PAD) is essential to secure iris
recognition systems. Recent iris PAD solutions achieved good performance by
leveraging deep learning techniques. However, most results were reported under
intra-database scenarios and it is unclear if such solutions can generalize
well across databases and capture spectra. These PAD methods run the risk of
overfitting because of the binary label supervision during the network
training, which serves global information learning but weakens the capture of
local discriminative features. This chapter presents a novel attention-based
deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise
supervision to capture the fine-grained pixel/patch-level cues and attention
mechanism to guide the network to automatically find regions where most
contribute to an accurate PAD decision. Extensive experiments are performed on
six NIR and one visible-light iris databases to show the effectiveness and
robustness of proposed A-PBS methods. We additionally conduct extensive
experiments under intra-/cross-database and intra-/cross-spectrum for detailed
analysis. The results of our experiments indicates the generalizability of the
A-PBS iris PAD approach.
