  Face Presentation Attack Detection (PAD) has drawn increasing attentions to
secure the face recognition systems that are widely used in many applications.
Conventional face anti-spoofing methods have been proposed, assuming that
testing is from the same domain used for training, and so cannot generalize
well on unseen attack scenarios. The trained models tend to overfit to the
acquisition sensors and attack types available in the training data. In light
of this, we propose an end-to-end learning framework based on Domain Adaptation
(DA) to improve PAD generalization capability. Labeled source-domain samples
are used to train the feature extractor and classifier via cross-entropy loss,
while unsupervised data from the target domain are utilized in adversarial DA
approach causing the model to learn domain-invariant features. Using DA alone
in face PAD fails to adapt well to target domain that is acquired in different
conditions with different devices and attack types than the source domain. And
so, in order to keep the intrinsic properties of the target domain, deep
clustering of target samples is performed. Training and deep clustering are
performed end-to-end, and experiments performed on several public benchmark
datasets validate that our proposed Deep Clustering guided Unsupervised Domain
Adaptation (DCDA) can learn more generalized information compared with the
state-of-the-art classification error on the target domain.
