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

Face anti-spoofing plays a crucial role in protecting face recognitionsystems from various attacks. Previous model-based and deep learning approachesachieve satisfactory performance for 2D face spoofs, but remain limited formore advanced 3D attacks such as vivid masks. In this paper, we address 3D faceanti-spoofing via the proposed Hypergraph Convolutional Neural Networks(HGCNN). Firstly, we construct a computation-efficient and posture-invariantface representation with only a few key points on hypergraphs. The hypergraphrepresentation is then fed into the designed HGCNN with hypergraph convolutionfor feature extraction, while the depth auxiliary is also exploited for 3D maskanti-spoofing. Further, we build a 3D face attack database with color, depthand infrared light information to overcome the deficiency of 3D faceanti-spoofing data. Experiments show that our method achieves thestate-of-the-art performance over widely used 3D and 2D databases as well asthe proposed one under various tests.

Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks