  Inspired by the philosophy employed by human beings to determine whether a
presented face example is genuine or not, i.e., to glance at the example
globally first and then carefully observe the local regions to gain more
discriminative information, for the face anti-spoofing problem, we propose a
novel framework based on the Convolutional Neural Network (CNN) and the
Recurrent Neural Network (RNN). In particular, we model the behavior of
exploring face-spoofing-related information from image sub-patches by
leveraging deep reinforcement learning. We further introduce a recurrent
mechanism to learn representations of local information sequentially from the
explored sub-patches with an RNN. Finally, for the classification purpose, we
fuse the local information with the global one, which can be learned from the
original input image through a CNN. Moreover, we conduct extensive experiments,
including ablation study and visualization analysis, to evaluate our proposed
framework on various public databases. The experiment results show that our
method can generally achieve state-of-the-art performance among all scenarios,
demonstrating its effectiveness.
