Occlusion Aware Student Emotion Recognition based on Facial Action Unit Detection

arXiv:2307.09465v2 Announce Type: replace
Abstract: Given that approximately half of science, technology, engineering, and mathematics (STEM) undergraduate students in U.S. colleges and universities leave by the end of the first year [15], it is crucial to improve the quality of classroom environments. This study focuses on monitoring students' emotions in the classroom as an indicator of their engagement and proposes an approach to address this issue. The impact of different facial parts on the performance of an emotional recognition model …

abstract arxiv classroom colleges cs.cv detection emotion emotions engineering environments mathematics monitoring quality recognition science stem students study technology the end type undergraduate universities