Face Anti Spoofing

Face anti-spoofing (FAS) aims to secure facial recognition systems by detecting fake faces (spoofs) presented as legitimate identities. Current research heavily focuses on improving the generalization of FAS models across diverse domains (different cameras, lighting, attack types), employing architectures like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), often enhanced by techniques such as data augmentation, multimodal fusion, and self-supervised learning. The development of robust and generalized FAS methods is crucial for the widespread adoption of secure facial recognition technologies in various applications, from access control to financial transactions.