Music Recommendation through Facial Emotion Detection using Deep Learning

Abstract

Abstract: We cannot imagine our lives without music. Only commercially produced music is played for users. The selection of the main features is an enormously important issue for systems like facial expression recognition. The recommended strategy helps individuals in their musical listening by providing recommendations based on emotions, feelings, and sentiments. The seven facial emotion categories that have been considered are angry, disgusted, fear, pleased, sad, surprise, and neutral—are meant to be specifically allocated to each identified face. To classify the emotion, the object should be detected from an inputted image. The object can be recognized in the image using the Haar-Cascades technique. This algorithm can be defined in different stages: Calculating Haar Features, Creating Integral Images, BiLSTM, and Implementing Cascading Classifiers. A deep learning model called BiLSTM (Bidirectional Long Short-Term Memory) is used to categorize human emotion. Based on the predicted emotion the music is mapped and the playlist is recommended to the user. The k-means clustering algorithm is used to map the music to the expected emotion, as compared to the existing models the deep learning model BiLSTM will give the best performance and 86.5% accuracy