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.

中文翻译：

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DRL-FAS：基于深度强化学习的面部反欺骗新框架

受人类用来确定所提出的面部实例是否真实的哲学启发，即首先在全球范围内浏览该实例，然后仔细观察局部区域以获取更多的歧视性信息，以解决面部反欺骗问题，我们提出了一种基于卷积神经网络（CNN）和递归神经网络（RNN）的新颖框架。特别是，我们通过利用深度强化学习对从图像子补丁中探索与面部欺骗有关的信息进行建模。我们进一步介绍了一种递归机制，以使用RNN从探索的子补丁中顺序学习局部信息的表示形式。最后，出于分类的目的，我们将局部信息与全局信息融合在一起，可以通过CNN从原始输入图像中学习局部信息。此外，我们进行了广泛的实验，包括消融研究和可视化分析，以在各种公共数据库上评估我们提出的框架。实验结果表明，我们的方法通常可以在所有情况下达到最先进的性能，证明了其有效性。