An Efficient Model for Facial Expression Recognition with Music Recommendation

Abstract

An AI interactive robot can identify human faces, determine the emotions of the person it is chatting with, and then pick appropriate replies using algorithms that analyze facial expressions and recognize faces. One example of these algorithms is facing recognition and emotion recognition algorithms. Deep learning is currently the most effective method for carrying out tasks. We have developed a real-time system that can recognize human faces, determine human emotions, and even provide users with music recommendations by utilizing deep learning and a few Python modules. The OAHEGA and FER-2013 datasets train the models presented in this article. The accuracy of our suggested model was compared to several baseline approaches, and the results were quite affirmative. Anger, fear, pleasure, neutral, sorrow, and surprise are the six feelings that our CNN model can predict.