  Face anti-spoofing (FAS) plays a crucial role in securing face recognition
systems. Empirically, given an image, a model with more consistent output on
different views of this image usually performs better, as shown in Fig.1.
Motivated by this exciting observation, we conjecture that encouraging feature
consistency of different views may be a promising way to boost FAS models. In
this paper, we explore this way thoroughly by enhancing both Embedding-level
and Prediction-level Consistency Regularization (EPCR) in FAS. Specifically, at
the embedding-level, we design a dense similarity loss to maximize the
similarities between all positions of two intermediate feature maps in a
self-supervised fashion; while at the prediction-level, we optimize the mean
square error between the predictions of two views. Notably, our EPCR is free of
annotations and can directly integrate into semi-supervised learning schemes.
Considering different application scenarios, we further design five diverse
semi-supervised protocols to measure semi-supervised FAS techniques. We conduct
extensive experiments to show that EPCR can significantly improve the
performance of several supervised and semi-supervised tasks on benchmark
datasets. The codes and protocols will be released at
https://github.com/clks-wzz/EPCR.
