ExpressNet-MoE: A hybrid deep neural network for emotion recognition

In many domains, including online education, healthcare, security, and human–computer interaction, facial emotion recognition (FER) is essential. Real-world FER is still difficult because of factors like head positions, occlusions, illumination shifts, and demographic diversity. Engagement detection system, which is essential in virtual learning platforms is severely challenged by these factors. In this article, we propose ExpressNet-MoE, a novel hybrid deep learning architecture that combines Convolutional Neural Networks (CNNs) with a Mixture of Experts (MoE) framework to address these challenges. The proposed model dynamically selects the most relevant expert networks for each input, thereby improving generalization and adaptability across diverse datasets. Our methodology involves training ExpressNet-MoE independently on several benchmark datasets after preprocessing facial pictures using BlazeFace for face detection and alignment. To maintain class distribution, stratified sampling is used to divide each dataset into training and testing groups. Our model improves on the accuracy of emotion recognition by utilizing multi-scale feature extraction to collect both global and local facial features. ExpressNet-MoE includes numerous CNN-based feature extractors, a MoE module for adaptive feature selection, and finally a residual network backbone for deep feature learning. To demonstrate efficacy of our proposed model we evaluated it on four widely used datasets: AffectNet 7 , AffectNet 8 , RAF-DB, and FER-2013; and compared with current state-of-the-art methods. Our model achieves accuracies of 74.40% ± 0 . 45 on AffectNet 7 , 71.98% ± 0 . 66 on AffectNet 8 , 83.41% ± 1 . 06 on RAF-DB, and 67.05% ± 2 . 08 on FER-2013. Overall, the findings indicate that the adaptive expert selection and multi-scale feature extraction significantly enhances the robustness of facial emotion recognition across diverse real-world conditions and how it may be used to develop end-to-end emotion recognition systems in practical settings. Reproducible codes and results are made publicly accessible at https://github.com/DeeptimaanB/ExpressNet-MoE . • Effective, domain adaptive and multi-scale feature extraction approach. • Demonstrating improved generalizaton across multiple datasets.