  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
visual 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 a measurement set, evaluation protocol and training/validation/testing
subsets, developing a new benchmark for face anti-spoofing. Moreover, we
present a new multi-modal fusion method as baseline, which performs feature
re-weighting to select the more informative channel features while suppressing
the less useful ones for each modal. 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/chalearnfacespoofingattackdete
