Machine Learning based Emotion Recognition using Facial Action Units over Edge-based Academic Infrastructure
DOI:
https://doi.org/10.62019/abbdm.v5i1.286Abstract
Edge-based applications are envisaged to have a paramount impact on the academic landscape. One of the key aspects of academic activities is to keep the learners engaged. The pertinent engagement activities require constant analysis of the learner’s emotions and expressions. Both aspects of comprehending the learner’s engagement greatly rely on facial coding/recognition systems. Facial recognition widely depends upon the component analysis of facial action units which are structured sets of semantic features. This research proposes a lightweight machine learning-based framework for edge-driven gadgets with five major modules and 68 recognized points of interest in each image of 360x360 dimensions. It exploits Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) to recognize the audience, capture expressions and semantically label emotions. Hence, a real-time adaptive feedback-based experience can be enhanced through dynamic interaction for improvising the outcomes of learning activities. The Extended Cohn-Kanade (CK+) dataset has been employed through an extraction function that classifies several learner emotions. The expressions and emotions of the audience during interaction were taken as input to the proposed model and maintained in the dataset along with the output. This feedback identified the spectators’ requirement for better interaction and more focus on engaging the learners. An overall accuracy of 98% was achieved in correctly predicting the learner's emotions by the proposed approach, as highlighted through standard machine learning metrics. The potential future directions are deep-learning-based gesture analysis, improving the precision for spectator feedback and efficiently catering for the computational requirements.

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Copyright (c) 2025 Muhammad Ahmad Shahid, Muhammad Safyan, Abdullah Mustafa

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