  Face presentation attack detection (PAD) has been extensively studied by
research communities to enhance the security of face recognition systems.
Although existing methods have achieved good performance on testing data with
similar distribution as the training data, their performance degrades severely
in application scenarios with data of unseen distributions. In situations where
the training and testing data are drawn from different domains, a typical
approach is to apply domain adaptation techniques to improve face PAD
performance with the help of target domain data. However, it has always been a
non-trivial challenge to collect sufficient data samples in the target domain,
especially for attack samples. This paper introduces a teacher-student
framework to improve the cross-domain performance of face PAD with one-class
domain adaptation. In addition to the source domain data, the framework
utilizes only a few genuine face samples of the target domain. Under this
framework, a teacher network is trained with source domain samples to provide
discriminative feature representations for face PAD. Student networks are
trained to mimic the teacher network and learn similar representations for
genuine face samples of the target domain. In the test phase, the similarity
score between the representations of the teacher and student networks is used
to distinguish attacks from genuine ones. To evaluate the proposed framework
under one-class domain adaptation settings, we devised two new protocols and
conducted extensive experiments. The experimental results show that our method
outperforms baselines under one-class domain adaptation settings and even
state-of-the-art methods with unsupervised domain adaptation.
