  In this work, we study multi-domain learning for face anti-spoofing(MD-FAS),
where a pre-trained FAS model needs to be updated to perform equally well on
both source and target domains while only using target domain data for
updating. We present a new model for MD-FAS, which addresses the forgetting
issue when learning new domain data, while possessing a high level of
adaptability. First, we devise a simple yet effective module, called spoof
region estimator(SRE), to identify spoof traces in the spoof image. Such spoof
traces reflect the source pre-trained model's responses that help upgraded
models combat catastrophic forgetting during updating. Unlike prior works that
estimate spoof traces which generate multiple outputs or a low-resolution
binary mask, SRE produces one single, detailed pixel-wise estimate in an
unsupervised manner. Secondly, we propose a novel framework, named FAS-wrapper,
which transfers knowledge from the pre-trained models and seamlessly integrates
with different FAS models. Lastly, to help the community further advance
MD-FAS, we construct a new benchmark based on SIW, SIW-Mv2 and Oulu-NPU, and
introduce four distinct protocols for evaluation, where source and target
domains are different in terms of spoof type, age, ethnicity, and illumination.
Our proposed method achieves superior performance on the MD-FAS benchmark than
previous methods. Our code and newly curated SIW-Mv2 are publicly available.

Computer Vision and Pattern Recognition