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:微表达度识别(MER)吸引了广泛的研究兴趣,因为微观表达方式(ME)可以推断出真实情感; 先前的信息可以指导模型,有效学习有区别的ME特征; 然而,大多数工作的重点是研究具有更强代表性的一般模型,以适应性地综合ME流动信息的整体方式,这可能忽视ME先前的信息和特性; 为解决这一问题,先前的信息是,根据面部不同组成部分行动之间的关系可以推断出ME类别,这项工作设计了一个符合先前信息的新模式,并以可解释的方式学习ME的移动特征; 具体地说,本文建议采用一个分解和基于重建的图表代表学习模式(De-GRL)模式,以有效学习高层次的ME特征。 De-GRL包括两个模块:行动分解模块(ADM)和关系重建模块(RMM),在该模块学习面部关键组成部分的行动特征,RRRM探讨这些行动特征之间的关系。 在面部关键组成部分的基础上,DM将图表中提取的几何运动特征分为以重建为基础的图表结构结构结构,然后将基于图表的模型的模型的模型和分级结构结构结构,将所有的拟议行动从这些分级结构学习了这些分级矩阵,然后学习了这些分级的行动特征。</s>