# Could Micro-Expressions be Quantified? Electromyography Gives Affirmative Evidence

arXiv: https://arxiv.org/abs/2409.00017 · HTML: https://mlsanity.com/paper/2409.00017/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [MMME: A Spontaneous Multi-Modal Micro-Expression Dataset Enabling Visual-Physiological Fusion](https://mlsanity.com/paper/2506.09834/) (2025)
- [Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling](https://mlsanity.com/paper/2511.16952/) (2025)
- [CDER-SME: A Cross-Device Event-RGB Micro-Expression Dataset under Multi-Level Stress Induction](https://mlsanity.com/paper/2606.20715/) (2026)

## Abstract (arXiv)

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.