Liveness score-based regression neural networks for face anti-spoofing
This work addresses face anti-spoofing for security applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of face anti-spoofing by proposing a liveness score-based regression network to reduce dependency on third-party networks and user-defined labels, achieving state-of-the-art performance on four benchmarks in intra- and cross-dataset tests.
Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generating pseudo maps and the way in which the users define the labels. In this paper, we propose a liveness score-based regression network for overcoming the dependency on third party networks and users. First, we introduce a new labeling technique, called pseudo-discretized label encoding for generating discretized labels indicating the amount of information related to real images. Secondly, we suggest the expected liveness score based on a regression network for training the difference between the proposed supervision and the expected liveness score. Finally, extensive experiments were conducted on four face anti-spoofing benchmarks to verify our proposed method on both intra-and cross-dataset tests. The experimental results show our approach outperforms previous methods.