Music Recommendation System based on Facial Expressions by using CNN

Abstract: In today's world, the integration of emotion detection and personalized music recommendation has become increasingly prevalent across diverse fields. This project endeavors to develop a cutting-edge music recommendation system that tailors playlists to users' emotional states, as discerned from their facial expressions in real-time. Leveraging the Facial Expression Recognition 2013 (FER 2013) dataset, which encompasses a wide array of emotional expressions including happiness, sadness, anger, fear, disgust, neutrality, and surprise, serves as the foundation for training our model. The project employs the Mediapipe algorithm for feature extraction from facial expressions and utilizes a Convolutional Neural Network (CNN) to classify these emotions accurately. For seamless integration and user interaction, streamlit web browser frameworks are harnessed to facilitate music recommendations within an intuitive interface. Furthermore, the proposed system capitalizes on real-time face detection through camera input using OpenCV, ensuring timely and responsive interaction. By automatically generating music playlists based on the user's current emotional state, our system aims to significantly reduce computational time and overall costs compared to existing literature. In essence, this project presents a comprehensive solution that combines real-time facial expression analysis with personalized music recommendation, offering enhanced efficiency and accuracy while minimizing computational overhead.