Efficient Multi-Class Facial Emotion Recognition using YOLOv9: A Deep Learning Approach for Real-Time Applications

This work introduces a revolutionary YOLOv9 deep learning architecture-based method for facial emotion identification. Happy, Sad, Angry, Disgust, Natural, and Surprise are the six emotional classifications the research divides the 21,263 images into which the research divides. The model was trained on 88% of the dataset; 4% was used for testing and 8% for validation. The preprocessing operations were auto-orientation, zoom, rotation, and 416x416 pixel scaling. Training time was cut down significantly because the experiment was carried out with a T4 GPU from Kaggle. Five epochs later, the average mAP score of 0.85, average precision of 0.74, and average recall of 0.84 indicate encouraging emotional performance. At a mAP score of 0.98, the model showed exceptionally high accuracy in identifying disgust. With its reliable and effective approach to real-time emotion recognition in various applications, this work advances the field of affective computing.