微表情面部肌电跨模态分析及标注算法OACHSSCDCSTPCD

For a long time,the issue of limited samples has been a major hindrance to the development of micro-expression analysis,and this limitation primarily stems from the inherent difficulty in annotating micro-expression data.In this research,we aim to address this challenge by leveraging facial electromyography as a technical approach and propose three solutions for micro-expression data annotation:automatic annotation,semi-automatic annotation,and unsupervised annotation.Specifically,we first present an automatic micro-expression annotation system based on distal facial electromyography.Second,we propose a semi-automatic annotation scheme for micro-expression onset and offset frames based on single-frame annotation.Finally,for unsupervised annotation,we introduce a cross-modal self-supervised learning algorithm based on electromyographic signals.Additionally,this research endeavors to explore the temporal and intensity characteristics of micro-expressions using the electromyography modality.