Abstract
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.
Abstract (translated)
在这项工作中，我们提出了一种基于新型用户交互场景的创新合作视频面部活体检测方法。在这个新场景中，参与者被指示缓慢地将面向摄像头的脸部逐渐靠近镜头。这种受控接近脸部协议结合了光流分析，构成了我们方法的核心创新点。通过设计一个要求用户遵循特定移动模式的系统，我们能够利用神经网络光流估计技术稳健地提取面部体积信息，从而显著提升了真实人脸与各种展示攻击（包括打印照片、屏幕显示、面具和视频重播）之间的鉴别能力。 我们的方法处理预测的光流以及RGB帧，并通过一个神经分类器来分析这些数据。这种方法有效地利用了空间-时间特征，相比被动的方法而言，能够实现更可靠的活体检测。
URL
https://arxiv.org/abs/2508.10786
