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.

本文提出了一种基于生命得分回归网络的活体检测方法，通过引入一种称为伪分级标签编码的新标记方法来解决第三方网络的精度问题，进而在四个面部活体检测基准实验中取得了比以前方法更好的效果。