Authors:
Dennis Küster
1
;
Rathi Adarshi Rammohan
1
;
Hui Liu
1
;
Tanja Schultz
1
and
Rainer Koschke
2
Affiliations:
1
Cognitive Systems Lab, University of Bremen, Bremen, Germany
;
2
AG Software Engineering, University of Bremen, Bremen, Germany
Keyword(s):
Action Units, Electromyography, Facial Action Coding System, EMG, sEMG, fEMG, Subtle Expressions, Pattern Recognition, Machine Learning.
Abstract:
Facial expressions are at the heart of everyday social interaction and communication. Their absence, such as in Virtual Reality settings, or due to conditions like Parkinson’s disease, can significantly impact communication. Electromyography (EMG)-based facial action unit recognition (AUR) offers a sensitive and privacy-preserving alternative to video-based methods. However, while prior research has focused on peak intensity action units (AUs), there has been a lack of research on EMG-based AURs for lightweight recording of subtle expressions at multiple muscle sites. This study evaluates EMG-based AUR for both low- and high-intensity expressions across eight AUs using two types of mobile electrodes connected to the Biosignal Plux system. The results of four subjects indicate that even limited data may be sufficient to train reasonably accurate AUR models. Larger snap-on electrodes performed better for peak-intensity AUs, but smaller electrodes resulted in higher performance for low-
intensity expressions. These findings suggest that EMG-based AUR is viable for subtle expressions from short data segments and that smaller electrodes hold promise for future applications.
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