Self-supervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing
Researchers have developed a new one-class face anti-spoofing method called UFDANet. This technique disentangles liveness and domain features to improve robustness against unseen attacks. UFDANet also synthesizes novel liveness and domain features to enhance generalization capabilities, achieving performance comparable to existing two-class methods. AI
IMPACT Introduces a novel approach to enhance security in facial recognition systems by improving robustness against spoofing attempts.