Emotion-Based Music Movie and TV Series Recommendation System Using Deep Learning Algorithm

Abstract

It can be challenging to decide which music or movies to listen to from a huge number of options. The major purpose of our music and movie recommendation system is to provide clients with selections that fit their tastes. An assessment of a user's facial expression may provide insight into their current emotional or mental state. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. It is feasible to assist a user in picking which music or movie to listen to or watch, by building a suggestion system. The face of the user is detected using the webcam. The snapshot of the user is taken based on their mood or feeling. It recognizes six facial expressions: angry, sad, fearful, joyful, surprised, and neutral. Based on the expression classification, the users are given three categories of recommendation as movies, music, or series based on their feelings. Seven different human facial expressions are classified using the Convolutional Neural Network (CNN) model. The Haar Cascade is an Object Detection Algorithm for recognizing faces in images and real-time video.