Music Recommendation System Based on Facial Expression using CNN

This music recommendation system focuses on the creation of an innovative music recommendation system designed to suggest songs tailored to users’ facial emotions and preferences derived from their online behavior. Extensive user details are scrutinized and summarized to comprehend individual musical tastes. Real-time facial emotion detection, coupled with social web content summarization, aids in the identification of singer names. The system recommends songs based on detected emotions and identified singer names, offering suggestions for popular songs and playlists. An insightful observation reveals users’ inclination toward happy songs during moments of joy, prompting a unique approach to address a recognized issue. Rather than exacerbating low moods with sad songs, the system recommends motivational or happy songs, showcasing its commitment to uplifting users. User studies affirm the precision and utility of the project, underscoring the overarching goal to provide a music recommendation system that not only caters to users’ musical preferences but also enhances their emotional well-being. The integration of continuous facial emotion detection and website summarization into the recommendation process adds a novel and dynamic facet to the system's capabilities.