Emotion-Based Music Recommendation System

In an increasingly fast-paced world, individuals often struggle to find things matching their current emotional state, leading to inefficiencies in emotional regulation, stress relief, and overall mental well-being. Traditional music recommendation systems rely on user inputs or previous playlist, which may not accurately capture a person’s real-time mood. There is a need for a more intuitive and responsive solution that can automatically detect and respond to an individual’s emotions to enhance their music listening experience. A Flask web application that utilizes facial expression recognition (FER) to detect emotions in real-time and recommend music that aligns with the detected mood. By integrating the FER 2013 dataset and Spotify API, system provides a personalized and dynamic music recommendation experience. Additionally, implemented a generative AI to deliver engaging content. The aim is to increase user retention and engagement by enriching theoverall music listening experience.