Coordinate-Attention Enhanced EfficientNet for Emotion Recognition in Virtual Learning Environments

In recent times, emotion recognition is a continuous evolving process used to understand an individual’s emotional state especially in virtual learning environments. While existing models made improvements, a consistent challenge is to accurately recognize subtle and complex facial emotions that change rapidly, which is common with students in dynamic online sessions. To address, the proposed method CA-EffiecientNet introduces an enhanced EfficientNet architecture integrated with a Coordinate Attention (CA) block, which is specifically designed to capture both fine-grained spatial details and long-range dependencies in facial emotions. The proposed CA-EffiecientNet model improves the models ability to identify subtle signs related to a student’s emotional state such as frustration or involvement. The virtual leaning system integrates this proposed CA-EffiecientNet model to continuously monitor learners activity and dynamically adapt educational content. The performance of the CA-EffiecientNet is evaluated on the KDEF and RAF-DB datasets, achieving superior results with the accuracy of 97.6 and 98.57 compared to existing ResNet with Convolutional Block Attention Module (CBAM). The results also demonstrate the efficiency of the proposed CA-EffiecientNet model supported by its integration of MBConv layers, which reduce computational load while preserving model robustness.