Could Micro-Expressions be Quantified? Electromyography Gives Affirmative Evidence

TL;DR

This study demonstrates that micro-expressions can be objectively quantified using facial electromyography, revealing their brief duration, low intensity, and limited controllability, and introduces a new public EMG-based micro-expression database.

Contribution

It provides the first objective EMG-based quantification of micro-expressions and introduces CASMEMG, a public database for further research on micro-expression mechanisms.

Findings

Micro-expressions are short and low in intensity.

Micro-expressions are less controllable than macro-expressions.

CASMEMG database offers EMG signals for micro-expression research.

Abstract

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.