Cooperative Face Liveness Detection from Optical Flow
This addresses security vulnerabilities in biometric systems by enhancing anti-spoofing, though it is incremental as it builds on existing optical flow and interaction methods.
The paper tackled face liveness detection by introducing a controlled user interaction where participants move their face closer to the camera, combined with optical flow analysis, resulting in improved discrimination against various presentation attacks.
In this work, we proposed a novel cooperative video-based face liveness detection method based on a new user interaction scenario where participants are instructed to slowly move their frontal-oriented face closer to the camera. This controlled approaching face protocol, combined with optical flow analysis, represents the core innovation of our approach. By designing a system where users follow this specific movement pattern, we enable robust extraction of facial volume information through neural optical flow estimation, significantly improving discrimination between genuine faces and various presentation attacks (including printed photos, screen displays, masks, and video replays). Our method processes both the predicted optical flows and RGB frames through a neural classifier, effectively leveraging spatial-temporal features for more reliable liveness detection compared to passive methods.