Music Player System Using Real-Time Facial Expression Detection

In today's society, stress has become an escalating concern, aggravated by economic uncertainties and rising living costs. Listening to music is a widely recognized stress-relief strategy. The existing music player applications lack the sophistication to select songs tailored to the user's emotional state. This limitation restricts the therapeutic potential of music. Effective stress relief is closely tied to aligning the music with the user's current mood. To address this critical issue, we propose developing a groundbreaking Music Player System that leverages real-time facial expression detection to provide music recommendations. In this project, Python and Flask will be utilized to develop a user-friendly and responsive platform for the users. It uses the Convolutional Neural Network (CNN) algorithm to provide real-time analysis of the user's facial expressions captured via a camera. This unique approach is intended to transform the music experience of the user by providing a dynamic playlist that is generated by processing the facial expressions of the user. Moreover, the system is designed to provide a smooth and personalized music experience, ensuring each song matches the user's emotions. This way, the system aims to relieve the stress of the user by providing emotion-based music recommendations.