DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
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DRL-FAS：一种基于深度强化学习的人脸反欺骗新框架
DOI：
10.1109/tifs.2020.3026553
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发表时间：
2020-09
影响因子：
6.8
通讯作者：
Rizhao Cai;Haoliang Li;Shiqi Wang-;Changsheng Chen;A. Kot
中科院分区：
文献类型：
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作者：
Rizhao Cai;Haoliang Li;Shiqi Wang-;Changsheng Chen;A. Kot
受人类用来确定所呈现的面部示例是否真实的哲学的启发，即，针对人脸反欺骗问题，提出了一种基于卷积神经网络（CNN）和递归神经网络（RNN）的人脸反欺骗算法框架。特别是，我们通过利用深度强化学习对从图像子块中探索面部欺骗相关信息的行为进行建模。我们还引入了一个循环机制，用RNN从探索的子补丁中顺序地学习局部信息的表示。最后，为了分类的目的，我们将局部信息与全局信息融合，这些信息可以通过CNN从原始输入图像中学习。此外，我们进行了广泛的实验，包括消融研究和可视化分析，以评估我们提出的框架在各种公共数据库。实验结果表明，该方法在所有场景下都能达到最佳性能，证明了其有效性。
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.