Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection

Understand

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).