Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptively aggregate ME movement information in a holistic way, which may ignore the prior information and properties of MEs. To solve this issue, driven by the prior information that the category of ME can be inferred by the relationship between the actions of facial different components, this work designs a novel model that can conform to this prior information and learn ME movement features in an interpretable way. Specifically, this paper proposes a Decomposition and Reconstruction-based Graph Representation Learning (DeRe-GRL) model to effectively learn high-level ME features. DeRe-GRL includes two modules: Action Decomposition Module (ADM) and Relation Reconstruction Module (RRM), where ADM learns action features of facial key components and RRM explores the relationship between these action features. Based on facial key components, ADM divides the geometric movement features extracted by the graph model-based backbone into several sub-features, and learns the map matrix to map these sub-features into multiple action features; then, RRM learns weights to weight all action features to build the relationship between action features. The experimental results demonstrate the effectiveness of the proposed modules, and the proposed method achieves competitive performance.

翻译：微表情识别（MER）因微表情（MEs）能够推断真实情感而引发了广泛的研究兴趣。先验信息可有效引导模型学习判别性微表情特征。然而，现有研究大多聚焦于设计具有更强表征能力的通用模型，以整体方式自适应聚合微表情运动信息，这可能导致忽略微表情的先验信息与特性。为解决该问题，本文受"微表情类别可通过面部不同组件动作间的关系推断"这一先验信息驱动，设计了一种能遵循该先验信息并以可解释方式学习微表情运动特征的新模型。具体而言，本文提出一种基于分解与重构的图表示学习（DeRe-GRL）模型，以高效学习高层微表情特征。DeRe-GRL包含两个模块：动作分解模块（ADM）与关系重构模块（RRM），其中ADM学习面部关键组件的动作特征，RRM探索这些动作特征间的关联。基于面部关键组件，ADM将由图模型主干提取的几何运动特征分解为若干子特征，并学习映射矩阵将这些子特征映射为多个动作特征；随后，RRM学习权重对所有动作特征进行加权，以构建动作特征间的关系。实验结果表明了所提模块的有效性，且该方法取得了具有竞争力的性能。