心理科学进展2024，Vol.32Issue(1)：1-13,13.DOI:10.3724/SP.J.1042.2024.00001
微表情面部肌电跨模态分析及标注算法
Cross-modal analysis of facial EMG in micro-expressions and data annotation algorithm
摘要
Abstract
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.关键词
图像标注/微表情分析/远端面部肌电/微表情数据标注Key words
image annotation/micro-expression analysis/distal facial electromyography/micro-expression data annotation分类
社会科学引用本文复制引用
王甦菁,王俨,李婧婷,东子朝,张建行,刘烨..微表情面部肌电跨模态分析及标注算法[J].心理科学进展,2024,32(1):1-13,13.基金项目
国家自然科学基金项目(62276252,U19B2032,62106256). （62276252,U19B2032,62106256）
