Facial Sentiment Analysis Using Convolutional Neural Network and Fuzzy Systems

This study provides a detailed study of a Convolutional Neural Network (CNN) model optimized for facial expression recognition with Fuzzy logic using Fuzzy2DPooling and Fuzzy Neural Networks (FNN), and discusses data augmentation in model optimization. It highlights important roles. performance. First, the effectiveness of the models in classifying emotions from FER2013, RAB-DB, and CK+ datasets was evaluated by a 5-fold cross-validation method, which showed that the accuracy varied widely among different emotion classes and was affected by overfitting. It turned out to be easy. The integration of data augmentation techniques, including random rotation, translation, and inversion, significantly improved the model's generalization capabilities. This was evidenced by higher accuracy and more consistent loss curves observed across all folds. After augmentation, the model showed significant improvement, achieving average test accuracies of 98.95% on FER2013, 99.99% on RAF-DB, and 100% on CK+ across all folds. Despite these advances, challenges specific to certain classes of emotions remain, highlighting the need for continued model refinement. This study concludes that data augmentation is an important step in developing robust facial expression recognition models and has potential benefits for a variety of applications requiring accurate emotion recognition.