Facial Expression based Music Recommendation System using Deep Convolutional Neural Network

The proposed context-aware music recommendation system revolutionizes user experience by combining cutting-edge face recognition technology with Convolutional Neural Network (CNN) algorithms. To overcome the challenges faced by traditional voice analysis methodologies, this research study not only enhances mood detection accuracy but also significantly reduces the training duration. Utilizing the Streamlit library for the user interface ensures seamless interaction across various expertise levels. In the backend, the synergy among TensorFlow, Keras API, and foundational frameworks like NumPy empowers the proposed system to efficiently analyze extensive datasets, delivering personalized music recommendations in real-time. This integration of face recognition, CNN algorithms, and robust frameworks signifies a technological leap, reshaping the music recommendation landscape by acknowledging the dynamic nature of emotions and the diverse contexts in which users engage with music.