  Special cameras that provide useful features for face anti-spoofing are
desirable, but not always an option. In this work we propose a method to
utilize the difference in dynamic appearance between bona fide and spoof
samples by creating artificial modalities from RGB videos. We introduce two
types of artificial transforms: rank pooling and optical flow, combined in
end-to-end pipeline for spoof detection. We demonstrate that using intermediate
representations that contain less identity and fine-grained features increase
model robustness to unseen attacks as well as to unseen ethnicities. The
proposed method achieves state-of-the-art on the largest cross-ethnicity face
anti-spoofing dataset CASIA-SURF CeFA (RGB).
