An enhanced GhostNet model for emotion recognition: leveraging efficient feature extraction and attention mechanisms.

Emotion recognition plays a crucial role in understanding decision-making processes, as emotional stimuli significantly influence individuals' choices. However, existing emotion recognition systems face challenges in handling complex natural environments, diverse emotional expressions, and limited data availability, hampering their effectiveness and widespread adoption. To address these issues, we propose an Enhanced GhostNet with Transformer Encoder (EGT) model that leverages deep learning techniques for robust emotion recognition through facial expressions. The EGT model integrates GhostNet's efficient feature extraction, the Transformer's ability to capture global context, and a dual attention mechanism to selectively enhance critical features. Experimental results show that the EGT model achieves an accuracy of 89.3% on the RAF-DB dataset and 85.7% on the AffectNet dataset, outperforming current state-of-the-art lightweight models. These results indicate the model's capability to recognize various emotional states with high confidence, even in challenging and noisy environments. Our model's improved accuracy and robustness in emotion recognition can enhance intelligent human-computer interaction systems, personalized recommendation systems, and mental health monitoring tools. This research underscores the potential of advanced deep learning techniques to significantly improve emotion recognition systems, providing better user experiences and more informed decision-making processes.