Improving Monocular Facial Presentation-Attack-Detection Robustness with Synthetic Noise Augmentations.

We present a synthetic augmentation approach towards improving monocular face presentation-attack-detection (PAD) robustness to real-world noise additions. Face PAD algorithms secure authentication systems against spoofing attacks, such as pictures, videos, and 2D-inspired masks. Best-in-class PAD methods typically use 3D imagery, but these can be expensive.