  Face anti-spoofing is crucial to security of face recognition systems.
Previous approaches focus on developing discriminative models based on the
features extracted from images, which may be still entangled between spoof
patterns and real persons. In this paper, motivated by the disentangled
representation learning, we propose a novel perspective of face anti-spoofing
that disentangles the liveness features and content features from images, and
the liveness features is further used for classification. We also put forward a
Convolutional Neural Network (CNN) architecture with the process of
disentanglement and combination of low-level and high-level supervision to
improve the generalization capabilities. We evaluate our method on public
benchmark datasets and extensive experimental results demonstrate the
effectiveness of our method against the state-of-the-art competitors. Finally,
we further visualize some results to help understand the effect and advantage
of disentanglement.
