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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.00017 |
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| _version_ | 1866912009839181824 |
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| author | Li, Jingting Lu, Shaoyuan Wang, Yan Dong, Zizhao Wang, Su-Jing Fu, Xiaolan |
| author_facet | Li, Jingting Lu, Shaoyuan Wang, Yan Dong, Zizhao Wang, Su-Jing Fu, Xiaolan |
| contents | Micro-expressions (MEs) are brief, subtle facial expressions that reveal concealed emotions, offering key behavioral cues for social interaction. Characterized by short duration, low intensity, and spontaneity, MEs have been mostly studied through subjective coding, lacking objective, quantitative indicators. This paper explores ME characteristics using facial electromyography (EMG), analyzing data from 147 macro-expressions (MaEs) and 233 MEs collected from 35 participants. First, regarding external characteristics, we demonstrate that MEs are short in duration and low in intensity. Precisely, we proposed an EMG-based indicator, the percentage of maximum voluntary contraction (MVC\%), to measure ME intensity. Moreover, we provided precise interval estimations of ME intensity and duration, with MVC\% ranging from 7\% to 9.2\% and the duration ranging from 307 ms to 327 ms. This research facilitates fine-grained ME quantification. Second, regarding the internal characteristics, we confirm that MEs are less controllable and consciously recognized compared to MaEs, as shown by participants responses and self-reports. This study provides a theoretical basis for research on ME mechanisms and real-life applications. Third, building on our previous work, we present CASMEMG, the first public ME database including EMG signals, providing a robust foundation for studying micro-expression mechanisms and movement dynamics through physiological signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Could Micro-Expressions be Quantified? Electromyography Gives Affirmative Evidence Li, Jingting Lu, Shaoyuan Wang, Yan Dong, Zizhao Wang, Su-Jing Fu, Xiaolan Human-Computer Interaction Micro-expressions (MEs) are brief, subtle facial expressions that reveal concealed emotions, offering key behavioral cues for social interaction. Characterized by short duration, low intensity, and spontaneity, MEs have been mostly studied through subjective coding, lacking objective, quantitative indicators. This paper explores ME characteristics using facial electromyography (EMG), analyzing data from 147 macro-expressions (MaEs) and 233 MEs collected from 35 participants. First, regarding external characteristics, we demonstrate that MEs are short in duration and low in intensity. Precisely, we proposed an EMG-based indicator, the percentage of maximum voluntary contraction (MVC\%), to measure ME intensity. Moreover, we provided precise interval estimations of ME intensity and duration, with MVC\% ranging from 7\% to 9.2\% and the duration ranging from 307 ms to 327 ms. This research facilitates fine-grained ME quantification. Second, regarding the internal characteristics, we confirm that MEs are less controllable and consciously recognized compared to MaEs, as shown by participants responses and self-reports. This study provides a theoretical basis for research on ME mechanisms and real-life applications. Third, building on our previous work, we present CASMEMG, the first public ME database including EMG signals, providing a robust foundation for studying micro-expression mechanisms and movement dynamics through physiological signals. |
| title | Could Micro-Expressions be Quantified? Electromyography Gives Affirmative Evidence |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.00017 |