Improving Facial Emotion Recognition Through Ensemble Classification Strategies

Facial emotion recognition is vital in diverse domains such as human–computer interaction, healthcare, entertainment, and much more. However, accurately identifying emotions from facial expressions remains a challenging task due to factors such as variations in illumination, pose, and facial expressions. In order to address these challenges, this research paper presents an ensemble classification method that makes use of several traditional machine learning algorithms—such as SVM, XGBoost, random forest, and others—to determine the optimal approach for enhancing emotion recognition. Based on the results of each evaluation, the most effective approach for recognizing facial emotions was determined.