1 research outputs found
On Improving Temporal Consistency for Online Face Liveness Detection
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.Comment: technical repor