  Face anti-spoofing (a.k.a presentation attack detection) has drawn growing
attention due to the high-security demand in face authentication systems.
Existing CNN-based approaches usually well recognize the spoofing faces when
training and testing spoofing samples display similar patterns, but their
performance would drop drastically on testing spoofing faces of unseen scenes.
In this paper, we try to boost the generalizability and applicability of these
methods by designing a CNN model with two major novelties. First, we propose a
simple yet effective Total Pairwise Confusion (TPC) loss for CNN training,
which enhances the generalizability of the learned Presentation Attack (PA)
representations. Secondly, we incorporate a Fast Domain Adaptation (FDA)
component into the CNN model to alleviate negative effects brought by domain
changes. Besides, our proposed model, which is named Generalizable Face
Authentication CNN (GFA-CNN), works in a multi-task manner, performing face
anti-spoofing and face recognition simultaneously. Experimental results show
that GFA-CNN outperforms previous face anti-spoofing approaches and also well
preserves the identity information of input face images.
