Title: Region-Aware Instance Consistency Learning for Apex-free Micro-Expression Recognition
Primary area: applications to neuroscience & cognitive science
Keywords: Micro-expression Recognition, Affective Computing

Abstract: Micro-expression Recognition (MER) is challenging due to the subtle motion. Existing methods heavily rely on the onset/apex pair to capture the most discriminative motion clues. This paradigm struggles with labor-intensive apex annotation and effective utilization of data. In this paper, we propose a novel apex-free paradigm for MER that eliminates the need for expensive apex annotations while effectively capturing subtle motion dynamics. Our key insight is that multiple frames near the sequence midpoint exhibit spatially consistent and intensity varied motion cues relative to the onset frame. Motivated by this, our method treats each sequence as a set of multiple onset/near-median motion instances. To fully exploit weaker motion information conveyed by these diverse instances, our framework introduces an Instance Region Consistency (IRC) module that enforces visual attention consistency on similar facial activation regions across different instances within the same set. Furthermore, we present a Multi-Region Discovery (MRD) module with self-supervised learning to expand attention on more subtle activation regions which are typically neglected. Extensive experiments on four public micro-expression datasets demonstrate that our proposed approach surpasses state-of-the-art methods without any apex frame annotations.