Hybrid Deep Autoencoder and AdaBoost for Robust Facial Expression Recognition

Facial expression recognition (FER) remains a challenging task due to variations in facial features, occlusions, and imbalanced datasets, which often lead to misclassification of similar emotions. To address these challenges, this study proposes a hybrid Deep Autoencoder and AdaBoost model, leveraging deep feature extraction and ensemble learning to enhance classification robustness. The experimental evaluation on three benchmark datasets—MMAFEDB, AffectNet, and JAFFE—demonstrates outstanding performance, with the model achieving an AUC and Accuracy of 99.9% and 99.8% on large-scale datasets, while maintaining a strong performance of 94.9% AUC and 91.1% accuracy on smaller datasets. The confusion matrix analysis confirms the model's ability to accurately classify distinct emotions, with minor misclassifications occurring in expressions with overlapping features. These findings highlight the effectiveness of the proposed approach in improving FER accuracy, offering significant benefits for real-world applications such as human-computer interaction, emotion-aware systems, and psychological analysis, while also suggesting future enhancements through domain adaptation and refined feature extraction techniques.