EmotiveNet: Advancing Facial Expression Recognition with CNN-Random Forest Integration

This research paper examines the performance of a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Random Forest classifiers for classifying 10 distinct categories. Utilizing a confusion matrix, this study assessed the model’s accuracy, identifying high performance in specific classes while pinpointing challenges in distinguishing between others. The findings reveal the model’s strengths in learning complex patterns and weaknesses in areas prone to misclassification. Future enhancements will focus on refining the model through advanced feature engineering, expanding training datasets, and exploring innovative machine-learning techniques to improve accuracy and reliability, aiming for broader applicability in real-world scenarios.