In this paper, we focus on improving the online face liveness detection system to enhance the security of the downstream face recognition system. Most of the existing frame-based methods are suffering from the prediction inconsistency across time. To address the issue, a simple yet effective solution based on temporal consistency is proposed. Specifically, in the training stage, to integrate the temporal consistency constraint, a temporal self-supervision loss and a class consistency loss are proposed in addition to the softmax cross-entropy loss. In the deployment stage, a training-free non-parametric uncertainty estimation module is developed to smooth the predictions adaptively. Beyond the common evaluation approach, a video segment-based evaluation is proposed to accommodate more practical scenarios. Extensive experiments demonstrated that our solution is more robust against several presentation attacks in various scenarios, and significantly outperformed the state-of-the-art on multiple public datasets by at least 40% in terms of ACER. Besides, with much less computational complexity (33% fewer FLOPs), it provides great potential for low-latency online applications.

本研究旨在改善在线人脸活体检测系统，提高下游人脸识别系统的安全性。提出了一种基于时间一致性的简单而有效的解决方案，包括在训练阶段引入时间一致性约束的方法和开发出一个无需训练的非参数不确定性估计模块，以适应各种场景的演示攻击，并且在计算复杂度更低的情况下比现有方法在多个公共数据集上至少提高40％的ACER值，具有很大的潜力应用于低延迟的在线应用。

在线人脸活体检测中提高时间一致性