  Face anti-spoofing (FAS) plays a vital role in preventing face recognition
systems from presentation attacks. Existing face anti-spoofing datasets lack
diversity due to the insufficient identity and insignificant variance, which
limits the generalization ability of FAS model. In this paper, we propose Dual
Spoof Disentanglement Generation (DSDG) framework to tackle this challenge by
"anti-spoofing via generation". Depending on the interpretable factorized
latent disentanglement in Variational Autoencoder (VAE), DSDG learns a joint
distribution of the identity representation and the spoofing pattern
representation in the latent space. Then, large-scale paired live and spoofing
images can be generated from random noise to boost the diversity of the
training set. However, some generated face images are partially distorted due
to the inherent defect of VAE. Such noisy samples are hard to predict precise
depth values, thus may obstruct the widely-used depth supervised optimization.
To tackle this issue, we further introduce a lightweight Depth Uncertainty
Module (DUM), which alleviates the adverse effects of noisy samples by depth
uncertainty learning. DUM is developed without extra-dependency, thus can be
flexibly integrated with any depth supervised network for face anti-spoofing.
We evaluate the effectiveness of the proposed method on five popular benchmarks
and achieve state-of-the-art results under both intra- and inter- test
settings. The codes are available at
https://github.com/JDAI-CV/FaceX-Zoo/tree/main/addition_module/DSDG.
