A Novel RCNN-CNN Hybrid Framework for Emotion Recognition in Facial Expressions

In several domains like affective computing, neurology, and human-computer interfaces, facial emotion detection plays a significant role. This research explores an innovative approach of employing CNNs and RCNNs conjointly for facial emotion recognition. The combined model increases both the accuracy and efficiency in the identification and categorization of specific emotions from facial expressions by using the advantages of RCNN in region assignment and CNN in feature extraction. Impressive results were obtained from the evaluation of the integrated RCNN-CNN model: The following results were achieved: 98.7% accuracy, 98.5% precision, 98.9% recall, and 98.7% F1-Score. This empirical shows how good is the recommended method at pinpointing the emotions exactly. Further, the research focuses on how the technique can help in fields such as human-computer interaction, emotional computing, and psychology education. Toward this goal, improvements will be made in the model’s ability to go beyond basic interactions and explore its scalability and applicability in real-world scenarios. In a nutshell, this study signifies progress in the area of facial emotion identification as well as creates the potential for human-machine interactions to be more empathetic and more compassionate.