Music Recommendation System Based on Facial Emotion Detection Using Spotify API

In the world of entertainment, music holds considerable importance, especially for those who find joy in rhythmic experiences. Despite the abundance of streaming platforms that enable access to favorite songs, they often fall short in capturing the intricate emotional nuances of users. This research recognizes a spectrum of emotions, including fear, happiness, sadness, anger, and neutrality. Its goal is to enrich the user experience by developing a recommendation system that proposes songs based on the user's current emotional state. The emotion-driven recommendation engine has seamlessly integrated into Spotify, a well-known music streaming service, providing users with a smooth and individualized journey in exploring music. The system aims to simplify the user experience by eliminating the necessity for manual song searches and, instead, intuitively suggests tracks that resonate with the user's emotions. The Spotify API serves as a crucial tool for accessing curated playlists, enabling the retrieval of desired music from thoughtfully organized collections centered around specific themes or titles.