  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.
