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

作　　者：王甦菁 王俨 李婧婷 东子朝 张建行 刘烨 WANG Su-Jing;WANG Yan;Li Jingting;DONG Zizhao;ZHANG Jianhang;LIU Ye(CAS Key Laboratory of Behavioral Science,Institute of Psychology,Beijing 100101,China;Department of Psychology,University of Chinese Academy of Sciences,Beijing 100049,China;School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003,China;State Key Laboratory of Brain and Cognitive Science,Institute of Psychology,Chinese Academy of Sciences,Beijing 100039,China)

机构地区：[1]中国科学院行为科学重点实验室(中国科学院心理研究所),北京100101 [2]中国科学院大学心理学系,北京100039 [3]江苏科技大学计算机科学与工程学院,镇江212003 [4]中国科学院心理研究所,脑与认知科学国家重点实验室,北京100039

出　　处：《心理科学进展》2024年第1期1-13,共13页Advances in Psychological Science

基　　金：国家自然科学基金项目(62276252,U19B2032,62106256)。

摘　　要：长久以来,微表情的小样本问题始终制约着微表情分析的发展,而小样本问题归根到底是因为微表情的数据标注十分困难。本研究希望借助面部肌电作为技术手段,从微表情数据自动标注、半自动标注和无标注三个方面各提出一套解决方案。对于自动标注,提出基于面部远端肌电的微表情自动标注方案;对于半自动标注,提出基于单帧标注的微表情起止帧自动标注;对于无标注,提出了基于肌电信号的跨模态自监督学习算法。同时,本研究还希望借助肌电模态,对微表情的呈现时间和幅度等机理特征进行拓展研究。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 

分 类 号：B841[哲学宗教—基础心理学]