  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.
