Emotion Detection and Music Recommendation Using Deep Learning and Computer Vision

This report presents a comprehensive approach to integrating emotion detection with music recommendation systems, leveraging the power of deep learning and computer vision. The primary objective is to create a personalized music experience by analyzing a user's real-time emotional state through facial expressions. We propose a system that utilizes a Convolutional Neural Network (CNN) for accurate emotion recognition from live video feeds or static images. The detected emotions (e.g., happy, sad, angry, neutral) are then mapped to a curated music database, where songs are categorized or tagged based on their emotional valence and arousal. This mapping allows the system to recommend music that either matches or aims to influence the user's current mood, providing a more intuitive and empathetic user experience than traditional content-based or collaborative filtering methods. Experimental results demonstrate the effectiveness of the CNN model in emotion classification and the feasibility of generating emotionally intelligent music recommendations, opening new avenues for adaptive user interfaces and personalized media consumption.