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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the increasing variations of face presentation attacks, modelgeneralization becomes an essential challenge for a practical faceanti-spoofing system. This paper presents a generalized face anti-spoofingframework that consists of three tasks: depth estimation, face parsing, andlive/spoof classification. With the pixel-wise supervision from the faceparsing and depth estimation tasks, the regularized features can betterdistinguish spoof faces. While simulating domain shift with meta-learningtechniques, the proposed one-side triplet loss can further improve thegeneralization capability by a large margin. Extensive experiments on fourpublic datasets demonstrate that the proposed framework and training strategiesare more effective than previous works for model generalization to unseendomains.

Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss