Harmonic Moods: Personalized Music Suggestions via Facial Emotion Recognition

Abstract

A user’s emotion can be detected by his/her facial expressions. These expressions can be derived from the live feed via the system’s camera. A lot of research is being conducted in the field of Computer Vision and Machine Learning (ML)/Deep Learning (DL), where machines are trained to identify various human emotions. Machine Learning/Deep Learning provides various techniques through which human emotions can be detected. One such technique is to use CNN model with Keras, which generates a small size trained model and makes Android-ML integration easier. Music is a great connector. It unites us across markets, ages, backgrounds, languages, preferences, political leanings and income levels. Music players and other streaming apps have a high demand as these apps can be used anytime, anywhere and can be combined with daily activities, travelling, sports, etc. With the rapid development of mobile networks and digital multimedia technologies, digital music has become the mainstream consumer content sought by many young people. People often use music as a means of mood regulation, specifically to change a bad mood, increase energy level or reduce tension. Also, listening to the right kind of music at the right time may improve mental health. Thus, human emotions have a strong relationship with music. In our proposed system, a emotion-based music player is created using CNN model which performs real time emotion detection and suggests songs as per detected emotion. This becomes an additional feature to the traditional music player apps that come pre-installed in our mobile phones. An important benefit of incorporating emotion detection is customer satisfaction. The objective of this system is to analyze the users face, predict the expression of the user and suggest songs suitable to the recognized emotion.