Face Presentation Attack Detection

Face presentation attack detection (PAD) aims to secure face recognition systems by identifying spoofing attempts, such as using photos, videos, or masks. Current research focuses on improving the generalization of PAD models to unseen attacks and domains, employing techniques like multispectral imaging, 3D point cloud analysis, and novel deep learning architectures such as Vision Transformers and Generative Adversarial Networks. This field is crucial for enhancing the security and reliability of face recognition systems across various applications, from smartphone authentication to high-stakes security scenarios, and ongoing research addresses challenges like bias and privacy concerns related to training data.