Music Recommendation Using Facial Emotion Recognition

Abstract

It can be very befuddling for people to choose which music to tune in to from a wide run of alternatives accessible. Different proposal frameworks have been made for particular spaces like music, feasting, and shopping, catering to the user's inclinations. Our essential objective is to supply music recommendations that adjust with the user's taste. By analyzing facial expressions and client feelings, ready to pick up experiences into their current mental or enthusiastic state. Music and recordings offer a extraordinary opportunity to show clients with a huge number of choices based on their slants and past data. It is well known that humans make use of facial expressions to express more clearly what they want to say and the context in which they meant their words. More than 60 percent of the users believe that at a certain point of time the number of songs present in their songs library is so large that they are unable to figure out the song which they have to play. By developing a recommendation system, it could assist a user to make a decision regarding which music one should listen to helping the user to reduce his/her stress levels. The user would not have to waste any time in searching or to look up for songs and the best track matching the user’s mood is detected, and songs would be shown to the user according to his/her mood. The image of the user is captured with the help of a webcam. The user’s picture is taken and thenas per the mood/emotion of the user an appropriate song from the playlist of the user is shown matching the user’s requirement.