用户缓慢靠近摄像头，结合光流分析提升活体检测精度。
Cooperative Face Liveness Detection from Optical Flow
- 设计用户主动靠近的交互流程，配合光流提取面部立体信息。
- 在多种攻击（打印照、屏幕、面具、重放）下准确率显著提升。
- 适合需要高安全性的身份认证场景，如金融支付、门禁系统。
本文提出一种新型协同式视频活体检测方法，基于用户被引导缓慢将正面朝向的面部靠近摄像头的新交互场景。该受控逼近协议结合光流分析，构成方法的核心创新。通过设计遵循特定运动模式的系统，我们利用神经网络光流估计实现面部体积信息的鲁棒提取，显著提升了对真实人脸与各类呈现攻击（包括打印照片、屏幕显示、面具及视频重放）的区分能力。本方法同时处理预测的光流与RGB帧，通过神经分类器有效融合时空特征，相比被动方法更具可靠性。
原文摘要 · Abstract (English)
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.
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