论文标题
MER-GCN：基于图形卷积网络基于关系建模的微表达识别
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
论文作者
论文摘要
微表达（ME）是可以揭示真正感觉的脸部自发的，非自愿的运动。最近，越来越多的研究关注了这一领域，以梳理深度学习技术。动作单位（AUS）是反映面部肌肉运动的基本行动，许多研究已经采用了AU检测来对面部表情进行分类。但是，耗时的注释过程使得很难将AUS的组合与特定的情感类别相关联。受节点关系构建图卷积网络（GCN）的启发，我们提出了一个面向AU的端到端图形分类网络，即MER-GCN，即使用3D Convnets提取AU特征并应用GCN层来发现ME分类的AU节点之间的依赖性。据我们所知，这项工作是使用基于AUS的GCN进行微表达识别（MER）的第一个端到端体系结构。实验结果表明，我们的方法表现优于基于CNN的MER网络。
Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling. Recently, increasing researches have paid attention to this field combing deep learning techniques. Action units (AUs) are the fundamental actions reflecting the facial muscle movements and AU detection has been adopted by many researches to classify facial expressions. However, the time-consuming annotation process makes it difficult to correlate the combinations of AUs to specific emotion classes. Inspired by the nodes relationship building Graph Convolutional Networks (GCN), we propose an end-to-end AU-oriented graph classification network, namely MER-GCN, which uses 3D ConvNets to extract AU features and applies GCN layers to discover the dependency laying between AU nodes for ME categorization. To our best knowledge, this work is the first end-to-end architecture for Micro-Expression Recognition (MER) using AUs based GCN. The experimental results show that our approach outperforms CNN-based MER networks.
