Face Presentation Attack

Face presentation attack (FPA) detection, also known as face anti-spoofing, aims to safeguard facial recognition systems against malicious attempts to bypass authentication using fake faces (e.g., printed photos, replayed videos, or 3D masks). Current research focuses on improving the generalization ability of detection models, employing techniques like multispectral imaging, deep ensemble learning with frame skipping, and multi-task learning with specialized loss functions to handle diverse attack methods and unseen scenarios. The development of robust and generalizable FPA detection is crucial for securing various applications relying on facial recognition, including access control, financial transactions, and identity verification, and is driving innovation in areas such as synthetic data generation for privacy-preserving training.