Emotion Detection and Music Recommendation System
This is an incremental application for users seeking automated music therapy, but it uses existing methods on new data without broad impact.
The paper tackles real-time emotion detection using facial recognition and deep learning to recommend music based on detected moods, resulting in a system that automatically plays playlists from local storage to enhance emotional well-being through music therapy.
As artificial intelligence becomes more and more ingrained in daily life, we present a novel system that uses deep learning for music recommendation and emotion-based detection. Through the use of facial recognition and the DeepFace framework, our method analyses human emotions in real-time and then plays music that reflects the mood it has discovered. The system uses a webcam to take pictures, analyses the most common facial expression, and then pulls a playlist from local storage that corresponds to the mood it has detected. An engaging and customised experience is ensured by allowing users to manually change the song selection via a dropdown menu or navigation buttons. By continuously looping over the playlist, the technology guarantees continuity. The objective of our system is to improve emotional well-being through music therapy by offering a responsive and automated music-selection experience.