English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Iris Presentation Attack Detection (PAD) is essential to secure irisrecognition systems. Recent iris PAD solutions achieved good performance byleveraging deep learning techniques. However, most results were reported underintra-database scenarios and it is unclear if such solutions can generalizewell across databases and capture spectra. These PAD methods run the risk ofoverfitting because of the binary label supervision during the networktraining, which serves global information learning but weakens the capture oflocal discriminative features. This chapter presents a novel attention-baseddeep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wisesupervision to capture the fine-grained pixel/patch-level cues and attentionmechanism to guide the network to automatically find regions where mostcontribute to an accurate PAD decision. Extensive experiments are performed onsix NIR and one visible-light iris databases to show the effectiveness androbustness of proposed A-PBS methods. We additionally conduct extensiveexperiments under intra-/cross-database and intra-/cross-spectrum for detailedanalysis. The results of our experiments indicates the generalizability of theA-PBS iris PAD approach.

Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains