Emotional Harmony through Deep Learning: A Facial Expression-Based Music Therapy

This research paper focuses on creating an advanced Music Recommendation System through a five-phase approach. Beginning with Data Collection and Extraction, the project utilizes the FER dataset for face images and the Spotify Web API to gather a diverse collection of musical tracks. Subsequent Data Cleaning and Preprocessing procedures refine the datasets, including streamlining the FER dataset for essential emotions and organizing music data in a database for accessibility. The research's core lies in Model Building, employing a custom Convolutional Neural Network (CNN) model named FERNET for precise emotion classification. Rigorous Model Optimization ensures an impressive 96% accuracy rate in emotion classification. The project concludes with the development of the Mood-Music App, a user-friendly application integrating the FER module, Emotion Recognition module, and Songs Recommendation module. This app utilizes real-time emotion analysis to provide a personalized and immersive music discovery experience, revolutionizing user engagement with music. The research showcases a comprehensive methodology, from data collection to the deployment of a sophisticated app, contributing significantly to the field of Music Recommendation Systems.