  Face anti-spoofing is critical to the security of face recognition systems.
Depth supervised learning has been proven as one of the most effective methods
for face anti-spoofing. Despite the great success, most previous works still
formulate the problem as a single-frame multi-task one by simply augmenting the
loss with depth, while neglecting the detailed fine-grained information and the
interplay between facial depths and moving patterns. In contrast, we design a
new approach to detect presentation attacks from multiple frames based on two
insights: 1) detailed discriminative clues (e.g., spatial gradient magnitude)
between living and spoofing face may be discarded through stacked vanilla
convolutions, and 2) the dynamics of 3D moving faces provide important clues in
detecting the spoofing faces. The proposed method is able to capture
discriminative details via Residual Spatial Gradient Block (RSGB) and encode
spatio-temporal information from Spatio-Temporal Propagation Module (STPM)
efficiently. Moreover, a novel Contrastive Depth Loss is presented for more
accurate depth supervision. To assess the efficacy of our method, we also
collect a Double-modal Anti-spoofing Dataset (DMAD) which provides actual depth
for each sample. The experiments demonstrate that the proposed approach
achieves state-of-the-art results on five benchmark datasets including
OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, and the new DMAD. Codes will be
available at https://github.com/clks-wzz/FAS-SGTD.
