DRL-FAS : A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

42 Scopus Citations
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Original languageEnglish
Pages (from-to)937-951
Journal / PublicationIEEE Transactions on Information Forensics and Security
Online published24 Sept 2020
Publication statusPublished - 2021


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.

Research Area(s)

  • Cameras, deep learning, Face anti-spoofing, Faces, Feature extraction, Learning (artificial intelligence), Machine learning, Recurrent neural networks, reinforcement learning, Support vector machines