Music Recommendation System Using Facial Emotions

Abstract

Emotions play an important role in human life. Extracting human emotions is important because it conveys nonverbal communication cues that play an important role in interpersonal relations. In recent years, facial emotion detection has received massive attention, and many businesses have already utilized this technology to get real-time analytics and feedback from customers to help their business grow. Currently, we have to manually find playlists according to our mood, and it's time-consuming and stressful. Therefore, this process is made automated and simple in this project by proposing a recommendation system for emotion recognition that is capable of detecting the users' emotions and suggesting playlists that can improve their mood. Implementation of the proposed recommender system is performed using Caffemodel to detect faces and the MLP Classifier to detect facial emotions based on the KDEF dataset.