RealTime Emotion Based Music Recommendation System

Abstract

The �Real-Time Emotion-Based Music Recommendation System� is an intelligent application that detects human emotions through facial expressions and recommends music accordingly. It integrates computer vision, affective computing, and deep learning to deliver a personalized multimedia experience. The system utilizes a Convolutional Neural Network (CNN) model developed with TensorFlow Keras, using the trained model �emotion-model-7class.h5�, which classifies facial expressions into seven emotions: happy, sad, angry, disgust, fear, neutral, and surprise. Live webcam input is captured and pre-processed (grayscale conversion, resiz ing, normalization), and facial regions are detected using OpenCV�s Haar Cascade Classifier. The CNN performs feature extraction from facial landmarks and classi f ies the emotion. Once detected, the corresponding playlist is selected and played using the python-vlc library, ensuring real-time responsiveness as emotions change. Designed for robust real-world use, the system handles varying lighting, facial orientations, and diverse user profiles. It emphasizes performance, reliability, and cross-platform portability, enabling integration with smart assistants and enter tainment systems. The model was trained using image augmentation, the Adam optimizer, and categorical crossentropy loss over 100 epochs, achieving 60.5% training and 59.9% validation accuracy. The project demonstrates a practical and adaptive approach to real-time emotion-aware computing for personalized music recommendation.