  Face anti-spoofing has become an increasingly important and critical security
feature for authentication systems, due to rampant and easily launchable
presentation attacks. Addressing the shortage of multi-modal face dataset,
CASIA recently released the largest up-to-date CASIA-SURF Cross-ethnicity Face
Anti-spoofing(CeFA) dataset, covering 3 ethnicities, 3 modalities, 1607
subjects, and 2D plus 3D attack types in four protocols, and focusing on the
challenge of improving the generalization capability of face anti-spoofing in
cross-ethnicity and multi-modal continuous data. In this paper, we propose a
novel pipeline-based multi-stream CNN architecture called PipeNet for
multi-modal face anti-spoofing. Unlike previous works, Selective Modal Pipeline
(SMP) is designed to enable a customized pipeline for each data modality to
take full advantage of multi-modal data. Limited Frame Vote (LFV) is designed
to ensure stable and accurate prediction for video classification. The proposed
method wins the third place in the final ranking of Chalearn Multi-modal
Cross-ethnicity Face Anti-spoofing Recognition Challenge@CVPR2020. Our final
submission achieves the Average Classification Error Rate (ACER) of 2.21 with
Standard Deviation of 1.26 on the test set.
