Micro-expression recognition ( MER ) has attracted the attention of many researchers in the past decade. However, occlusion occurs for MER in real-world scenarios. In this paper, a challenging issue in MER that is interesting but unexplored, i.e., occlusion MER, is deeply investigated. First, to research MER under real-world occlusion conditions, synthetic occluded microexpression databases are created by using various community masks. Second, to suppress the influence of occlusion, a R egion-inspired R elation R easoning N etwork ( RRRN ) is proposed to model the relations between various facial regions. The RRRN consists of a backbone network, a region-inspired (RI) module and a relation reasoning (RR) module. More specifically, the backbone network aims to extract feature representations from different facial regions, the RI module is designed to compute the adaptive weight from the facial region itself based on the unobstructedness and importance of the region for suppressing the influence of occlusion using an attention mechanism, and the RR module exploits the progressive interactions among these regions by performing graph convolutions. Experiments are conducted on two tasks of MEGC 2018: the holdout-database evaluation task and the composite database evaluation task. Experimental results show that RRRN can be utilized to significantly explore the importance of facial regions and capture the cooperative complementary relationship of facial regions for MER. The results also demonstrate that RRRN outperforms the state-of-the-art approaches, especially with respect to occlusion, where RRRN is more robust.

中文翻译：

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基于区域启发关系推理网络的部分遮挡下基于目标类的微表情识别

微表情识别（ MER ) 在过去十年中引起了许多研究人员的关注。然而，在现实场景中 MER 会发生遮挡。在本文中，深入研究了 MER 中一个有趣但尚未探索的具有挑战性的问题，即遮挡 MER。首先，为了研究真实世界遮挡条件下的 MER，合成遮挡微表情数据库是通过使用各种社区掩码创建的。其次，为了抑制遮挡的影响，a区域启发关系推理网络 （ RRRN ）被提出来模拟各种面部区域之间的关系。RRRN 由骨干网络、区域启发 (RI) 模块和关系推理 (RR) 模块组成。更具体地说，主干网络旨在从不同的面部区域提取特征表示，RI 模块被设计为根据区域的通畅性和重要性计算面部区域本身的自适应权重，以使用注意力机制抑制遮挡的影响，并且 RR 模块通过执行图形卷积来利用这些区域之间的渐进交互。对 MEGC 2018 的两个任务进行了实验：holdout-database 评估任务和复合数据库评估任务。实验结果表明，RRRN 可用于显着探索面部区域的重要性并捕获面部区域对 MER 的协同互补关系。结果还表明 RRRN 优于最先进的方法，尤其是在遮挡方面，其中 RRRN 更稳健。