  We address the problem of face anti-spoofing which aims to make the face
verification systems robust in the real world settings. The context of
detecting live vs. spoofed face images may differ significantly in the target
domain, when compared to that of labeled source domain where the model is
trained. Such difference may be caused due to new and unknown spoof types,
illumination conditions, scene backgrounds, among many others. These varieties
of differences make the target a compound domain, thus calling for the problem
of the unsupervised compound domain adaptation. We demonstrate the
effectiveness of the compound domain assumption for the task of face
anti-spoofing, for the first time in this work. To this end, we propose a
memory augmentation method for adapting the source model to the target domain
in a domain aware manner. The adaptation process is further improved by using
the curriculum learning and the domain agnostic source network training
approaches. The proposed method successfully adapts to the compound target
domain consisting multiple new spoof types. Our experiments on multiple
benchmark datasets demonstrate the superiority of the proposed method over the
state-of-the-art.
