  There has been an increasing consensus in learning based face anti-spoofing
that the divergence in terms of camera models is causing a large domain gap in
real application scenarios. We describe a framework that eliminates the
influence of inherent variance from acquisition cameras at the feature level,
leading to the generalized face spoofing detection model that could be highly
adaptive to different acquisition devices. In particular, the framework is
composed of two branches. The first branch aims to learn the camera invariant
spoofing features via feature level decomposition in the high frequency domain.
Motivated by the fact that the spoofing features exist not only in the high
frequency domain, in the second branch the discrimination capability of
extracted spoofing features is further boosted from the enhanced image based on
the recomposition of the high-frequency and low-frequency information. Finally,
the classification results of the two branches are fused together by a
weighting strategy. Experiments show that the proposed method can achieve
better performance in both intra-dataset and cross-dataset settings,
demonstrating the high generalization capability in various application
scenarios.
