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

TL;DR

This paper presents a privacy-preserving, real-time engagement measurement method using facial landmarks and spatial-temporal graph convolutional networks, achieving state-of-the-art accuracy on student engagement datasets.

Contribution

It introduces a novel ordinal learning framework for training ST-GCNs with facial landmarks for engagement detection, enhancing accuracy and interpretability.

Findings

Improved engagement classification accuracy by 3.1% on EngageNet

Enhanced binary engagement detection accuracy by 1.5% on Online Student Engagement dataset

Method is lightweight, fast, and suitable for real-time deployment in virtual learning environments

Abstract

Engagement in virtual learning is crucial for a variety of factors including student 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 from videos via the MediaPipe deep learning solution. The extracted facial landmarks are fed to Spatial-Temporal Graph Convolutional Networks (ST-GCNs) to output the engagement level of the student in the video. To integrate the ordinal nature of the engagement variable into the training process, ST-GCNs undergo training in a novel ordinal learning framework based on transfer learning. Experimental results on two video student engagement measurement datasets show the superiority of the proposed method compared to previous methods with improved state-of-the-art on the EngageNet dataset with a 3.1% improvement in four-class engagement level classification accuracy and on the Online Student Engagement dataset with a 1.5% improvement in binary engagement classification accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the developed ST-GCNs to interpret the engagement measurements obtained by the proposed method in both the spatial and temporal domains. The relatively lightweight and fast ST-GCN and its integration with the real-time MediaPipe make the proposed approach capable of being deployed on virtual learning platforms and measuring engagement in real-time.