Human face expression recognition using deep learning model ( YOLO-V9)

Abstract

Facial expression recognition has emerged as a promising field of study, with numerous applications in areas such as human-computer interaction, emotion analysis, and mental health monitoring. This thesis presents the development and evaluation of a novel facial expression recognition system for real-time emotion detection and classification, with the aim of advancing the state-of-the-art in this rapidly evolving domain. The proposed system employs a deep learning-based approach, utilizing convolutional neural networks (CNNs) to extract and classify facial features from input images frames. The performance of the facial expression recognition system is rigorously evaluated on several benchmark datasets, as well as in real-world scenarios involving human-computer interaction. The findings of this research contribute to the growing body of knowledge in the field of facial expression recognition and highlight the potential of deep learning-based approaches for real-time emotion detection and classification.