  Face presentation attack detection (PAD) is essential to secure face
recognition systems primarily from high-fidelity mask attacks. Most existing 3D
mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask
identities, types of sensors, and a total number of videos; 2) low-fidelity
quality of facial masks. Basic deep models and remote photoplethysmography
(rPPG) methods achieved acceptable performance on these benchmarks but still
far from the needs of practical scenarios. To bridge the gap to real-world
applications, we introduce a largescale High-Fidelity Mask dataset, namely
CASIA-SURF HiFiMask (briefly HiFiMask). Specifically, a total amount of 54,600
videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of
sensors. Together with the dataset, we propose a novel Contrastive
Context-aware Learning framework, namely CCL. CCL is a new training methodology
for supervised PAD tasks, which is able to learn by leveraging rich contexts
accurately (e.g., subjects, mask material and lighting) among pairs of live
faces and high-fidelity mask attacks. Extensive experimental evaluations on
HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of
our method.
