A More Objective Quantification of Micro-Expression Intensity through Facial Electromyography

Shaoyuan Lu, Jingting Li, Yan Wang, Zizhao Dong, Su-Jing Wang, Xiaolan Fu

Abstract: Micro-expressions are facial expressions that individuals reveal when trying to hide their genuine emotions. It has potential applications in areas such as lie detection and national security. It is generally believed that micro-expressions have three essential characteristics: short duration, low intensity, and local asymmetry. Most previous studies have investigated micro-expressions based on the characteristic of short duration. To our knowledge, no empirical studies have been conducted on the low-intensity characteristic. In this paper, we use facial EMG for the first time to study the characteristic of low intensity for micro-expression. In our experiment, micro-expressions were elicited from subjects and simultaneously collected their facial EMG through the second-generation micro-expression elicitation paradigm. We collected and annotated 33 macro-expressions and 48 micro-expressions. By comparing the two indicators of EMG :(1) the percentage of apex value in maximum voluntary contraction (MVC%) and (2) the area under EMG signal curve (integrated EMG, iEMG), we found that the MVC% and iEMG of micro-expression were significantly smaller than that of macro-expression. The result demonstrates that the intensity of micro-expression is significantly smaller than that of macro-expression.