Melody Meets Emotion: Revolutionizing Music Recommendations Through Emotional Intelligence

Emotions significantly influence human behavior, including music preferences. To enhance user experience and emotional well-being, this project introduces an intelligent Emotion-Based Music Player that uses facial emotion recognition to recommend mood-appropriate online playlists. The system leverages DeepFace for emotion analysis and OpenCV for image capture, supporting real-time webcam input, image upload, and manual emoji selection. It identifies seven core emotions—happy, sad, angry, neutral, surprise, fear, and disgust—and maps each to a relevant online music query. The desktop application, built with Tkinter and styled using ttkbootstrap, adapts its interface dynamically based on the detected mood. A live detection mode allows continuous emotion tracking and automatic playlist updates without user intervention. Threading ensures smooth video streaming and responsive interaction. By integrating affective computing with music recommendation, this system provides a personalized and immersive listening experience that aligns with the user's emotional state. Keywords: Emotion Detection, Facial Expression Recognition, Music Recommendation, DeepFace, Affective Computing, Real-time Analysis, Personalized Playlist, Multimodal Interaction.