Developing a personality based song recommendation system

This paper presents an Emotion-Based Music Recommendation System that utilizes facial expression analysis to provide personalized music suggestions based on a user's real-time emotional state. By integrating computer vision, deep learning, and the YouTube Data API, the system detects emotions such as happiness, sadness, anger, and neutrality, mapping them to appropriate music moods. It further refines recommendations based on user preferences like genre and language, ensuring a customized experience. The system also offers a mood adjustment feature, allowing users to either embrace or alter their emotional state through music. With a secure authentication system and an intuitive user interface, this approach enhances emotional well-being by combining artificial intelligence and music, making recommendations more dynamic, adaptive, and engaging.