Real-Time Emotion-Aware Music Player Using Deep Learning from Webcam Feed

Abstract

This work presents an intelligent music recommendation system that responds to human emotions in real time using facial expressions. By leveraging Convolutional Neural Networks (CNN), the system detects and interprets a user's emotional state from a live webcam feed. Once an emotion is classified, the system uses collaborative filtering techniques to suggest music that matches the detected mood, creating a personalized listening experience. This innovative approach bridges emotional intelligence and artificial intelligence, offering users a more immersive and adaptive interaction with technology. The system holds potential in entertainment, therapy, and stress-relief applications, providing music as a dynamic emotional companion. It redefines music recommendation by prioritizing emotional relevance over historical listening patterns.