Emotional Based Music Recommendation System for Mental Wellness

This project presents an Emotion-Based Music Recommendation System designed to enhance mental wellness through intelligent and personalized music therapy. The system uses real-time facial ex- pression recognition via a webcam and Convolutional Neural Networks (CNNs) to identify users’ emotional states such as happy, sad, angry, neutral, or surprised. Based on the detected emotion, suitable music is recommended to positively influence mood, reduce stress, and support emotional balance. By integrating computer vision, deep learning, and an emotion- aware recommendation engine, the system offers a non-invasive, user-friendly, and cost-effective digital solution for mental health support and affective computing applications