Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks

arXiv:2403.17175v1 Announce Type: new
Abstract: Engagement in virtual learning is crucial for a variety of factors including learner satisfaction, performance, and compliance with learning programs, but measuring it is a challenging task. There is therefore considerable interest in utilizing artificial intelligence and affective computing to measure engagement in natural settings as well as on a large scale. This paper introduces a novel, privacy-preserving method for engagement measurement from videos. It uses facial landmarks, which carry no personally identifiable information, extracted …

abstract and compliance artificial artificial intelligence arxiv compliance computing cs.cv engagement graph intelligence measurement measuring natural networks performance spatial temporal type virtual virtual learning