Machine Learning Based Music Recommendation System Using Facial Expression

Abstract

Abstract: Music plays a key role in improving your well-being, as it is one of the key sources of entertainment and inspiration to keep you going. Recent studies have shown that people respond very positively to music and that music has a great effect on a person's brain activity. It is often better to listen your favorite music these days. This job focuses on systems that suggest songs to users based on their state of mind. This computer vision system uses components to determine a user's emotion from facial expressions. When an emotion is detected, the system will suggest songs with that emotion, saving users a lot of time manually selecting and playing songs. It enables the migration of computer vision techniques for such systems to automation. To achieve this goal, we use the algorithm to classify human expressions, game play, and music tracks according to their currently detected emotions. Reduces the effort and time required to manually search for songs in the list. Recognize human facial expressions by extracting facial features using Haar Cascade and CNN algorithms.