  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.
