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ClassMood: An intelligent platform for facial expression recognition in education

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-education
computer-vision
data-mining-and-machine-learning

Abstract

Facial expressions serve as a crucial non-verbal medium for conveying human emotions and psychological states. Facial Expression Recognition (FER) technology holds significant promise in fields such as human-computer interaction and affective computing. Traditional FER approaches rely heavily on handcrafted features and shallow classifiers, making them inadequate for robust recognition in complex environments. Moreover, most existing studies are conducted in controlled laboratory settings and require users to possess substantial programming knowledge to operate the models, limiting the direct applicability of their findings in real-world scenarios such as education and healthcare.

This study constructs a seven-class facial expression dataset based on two public datasets, CASME2 and MMEW, and proposes a facial expression recognition tool named ClassMood (http://8.137.14.239/), which integrates deep learning and an attention mechanism. The tool aims to promote practical applications of FER technology in classroom teaching, thereby supporting emotion perception and instructional optimization in educational settings.

ClassMood is built using a residual neural network enhanced with an attention mechanism module, which was selected through comparative evaluation across five machine learning and deep learning algorithms. This model achieves excellent accuracy on training data and reaches 67.53% accuracy on a real-world student expression dataset collected in classroom scenarios. This tool is user-friendly, offering functionalities such as prediction and feedback directly through a web interface.

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