Detection of Face Emotion and Music Recommendation System using Machine Learning

Face emotion detection has recently attracted a lot of interest because of its uses in computer vision and the field of human-computer interaction. Various methods and applications were suggested and put into use as a result of the ongoing research in this area. In this study, we present an emotion-recognition recommender system that can identify a user’s feelings and offer a selection of suitable songs that might lift his spirits. To gather information and enable us to give the users a selection of music tracks that are effective at lifting the users’ spirits, a quick search was undertaken to learn how music may impact the user mood in the short term. The suggested system recognizes emotions, and if the individual is feeling down, a special playlist including the best kinds of music will be played to lift his spirits. On the other hand, if a favorable mood is recognized, an appropriate playlist will be offered that contains several genres of music that will amplify the pleasant feelings. Principal Component Analysis (PCA) methods and the Fisher Face algorithm are used to implement the suggested recommender system.