EFFICIENT FACIAL EMOTION BASED MUSIC RECOMMENDATION SYSTEM

Abstract

key component of AI development systems is image processing. The capture of an efficient facial emotion using an image processing system was intended for recommendation-based preface systems. A significant amount of investigation is being carried out with the help of a number of different technologies, including computer vision (CNN) and deep learning (DL), also known as machine learning, in order to identify the various human emotions. Music has a remarkable capacity to convey ideas. In spite of differences in income, political leanings, preferences, and languages, it brings us together., backgrounds, ages, and markets. In daily activities, sports, and travel, people prefer to listen to music, and there are high-demand apps such as music players and other streaming services that enhance the requirement of music-recommended system which is based on imaging. Human emotions and music are connected to one another. In the proposed work, with the help of the CNN model, real-time emotions like happy, sad, disgust, surprise, neutral, angry, fear are detected and an emotion-based music player suggests songs per detected emotions. This emotion detection method can be adapted for mobile phones to the traditional preinstalled music player apps as an additional feature. Customer satisfaction is one of the benefits of incorporating emotion, which can be mapped to illiterate people as well. This paper objective is to evaluate the user's face, forecast their expression, and recommend a song that fits their mood.