  Face presentation attack detection (PAD) has been an urgent problem to be
solved in the face recognition systems. Conventional approaches usually assume
the testing and training are within the same domain; as a result, they may not
generalize well into unseen scenarios because the representations learned for
PAD may overfit to the subjects in the training set. In light of this, we
propose an efficient disentangled representation learning for cross-domain face
PAD. Our approach consists of disentangled representation learning (DR-Net) and
multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative
models that can disentangle PAD informative features from subject
discriminative features. The disentangled features from different domains are
fed to MD-Net which learns domain-independent features for the final
cross-domain face PAD task. Extensive experiments on several public datasets
validate the effectiveness of the proposed approach for cross-domain PAD.
