On Improving Temporal Consistency for Online Face Liveness Detection

• URL: http://arxiv.org/abs/2006.06756v1
• Date: Thu, 11 Jun 2020 19:19:47 GMT
• Title: On Improving Temporal Consistency for Online Face Liveness Detection
• Authors: Xiang Xu and Yuanjun Xiong and Wei Xia
• Abstract summary: We focus on improving the online face liveness detection system to enhance the security of the downstream face recognition system. To address the issue, a simple yet effective solution based on temporal consistency is proposed. In the training stage, to integrate the temporal consistency constraint, a temporal self-supervision loss and a class consistency loss are proposed. In the deployment stage, a training-free non-parametric uncertainty estimation module is developed to smooth the predictions adaptively.
• Score: 43.3347240592507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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