Automatic Facial Expression Recognition for Online Learning Engagement Using Computer Vision and Artificial Intelligence

Facial expressions provide essential cues about human emotions, cognitive states, and psychological conditions. Automatic Facial Expression Recognition (FER) systems have become increasingly important in several domains including healthcare monitoring, prison security management, and online education systems. This paper proposes an Artificial Intelligence based FER system that uses computer vision and deep learning techniques to automatically detect and classify facial emotions from images and video streams. The system utilizes Convolutional Neural Networks (CNN) combined with machine learning algorithms to identify emotional states such as happiness, sadness, anger, fear, surprise, disgust, and neutrality.

The proposed model processes facial images through preprocessing, feature extraction, and classification stages. The architecture integrates convolution layers, pooling layers, and fully connected neural networks for emotion classification. Experimental evaluation demonstrates high recognition accuracy and robust performance across multiple datasets. The system can assist and allow educators to evaluate student engagement during online learning sessions.

The results show that the proposed FER framework achieves high classification accuracy with improved generalization ability. The research highlights the potential of artificial intelligence in improving emotional monitoring systems in healthcare, security, and education sectors.