  Face anti-spoofing is crucial for the security of face recognition system, by
avoiding invaded with presentation attack. Previous works have shown the
effectiveness of using depth and temporal supervision for this task. However,
depth supervision is often considered only in a single frame, and temporal
supervision is explored by utilizing certain signals which is not robust to the
change of scenes. In this work, motivated by two stream ConvNets, we propose a
novel two stream FreqSaptialTemporalNet for face anti-spoofing which
simultaneously takes advantage of frequent, spatial and temporal information.
Compared with existing methods which mine spoofing cues in multi-frame RGB
image, we make multi-frame spectrum image as one input stream for the
discriminative deep neural network, encouraging the primary difference between
live and fake video to be automatically unearthed. Extensive experiments show
promising improvement results using the proposed architecture. Meanwhile, we
proposed a concise method to obtain a large amount of spoofing training data by
utilizing a frequent augmentation pipeline, which contributes detail
visualization between live and fake images as well as data insufficiency issue
when training large networks.
