  Regardless of the usage of deep learning and handcrafted methods, the dynamic
information from videos and the effect of cross-ethnicity are rarely considered
in face anti-spoofing. In this work, we propose a static-dynamic fusion
mechanism for multi-modal face anti-spoofing. Inspired by motion divergences
between real and fake faces, we incorporate the dynamic image calculated by
rank pooling with static information into a conventional neural network (CNN)
for each modality (i.e., RGB, Depth and infrared (IR)). Then, we develop a
partially shared fusion method to learn complementary information from multiple
modalities. Furthermore, in order to study the generalization capability of the
proposal in terms of cross-ethnicity attacks and unknown spoofs, we introduce
the largest public cross-ethnicity Face Anti-spoofing (CASIA-CeFA) dataset,
covering 3 ethnicities, 3 modalities, 1607 subjects, and 2D plus 3D attack
types. Experiments demonstrate that the proposed method achieves
state-of-the-art results on CASIA-CeFA, CASIA-SURF, OULU-NPU and SiW.
