Facial expression recognition through muscle synergies and estimation of facial keypoint displacements through a skin-musculoskeletal model using facial sEMG signals.

ABSTRACT: The development of facial expression recognition (FER) and facial expression generation (FEG) systems is essential to enhance human-robot interactions (HRI). The facial action coding system is widely used in FER and FEG tasks, as it offers a framework to relate the action of facial muscles and the resulting facial motions to the execution of facial expressions. However, most FER and FEG studies are based on measuring and analyzing facial motions, leaving the facial muscle component relatively unexplored. This study introduces a novel framework using surface electromyography (sEMG) signals from facial muscles to recognize facial expressions and estimate the displacement of facial keypoints during the execution of the expressions. For the facial expression recognition task, we studied the co

SUBMITTER: Shu L 

PROVIDER: S-EPMC11861201 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature