  Face anti-spoofing is essential to prevent face recognition systems from a
security breach. Much of the progresses have been made by the availability of
face anti-spoofing benchmark datasets in recent years. However, existing face
anti-spoofing benchmarks have limited number of subjects ($\le\negmedspace170$)
and modalities ($\leq\negmedspace2$), which hinder the further development of
the academic community. To facilitate face anti-spoofing research, we introduce
a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest
publicly available dataset for face anti-spoofing in terms of both subjects and
modalities. Specifically, it consists of $1,000$ subjects with $21,000$ videos
and each sample has $3$ modalities (i.e., RGB, Depth and IR). We also provide
comprehensive evaluation metrics, diverse evaluation protocols,
training/validation/testing subsets and a measurement tool, developing a new
benchmark for face anti-spoofing. Moreover, we present a novel multi-modal
multi-scale fusion method as a strong baseline, which performs feature
re-weighting to select the more informative channel features while suppressing
the less useful ones for each modality across different scales. Extensive
experiments have been conducted on the proposed dataset to verify its
significance and generalization capability. The dataset is available at
https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0
