🤖 AI Summary
This study addresses the lack of real-time responsiveness and personalization in emotional health interventions by proposing a lightweight, closed-loop affective music recommendation system. Methodologically, it integrates OpenCV-based real-time facial video capture with the lightweight DeepFace model for on-device facial emotion classification (six basic emotions), dynamically matching identified states to curated mood-regulating tracks from a locally stored, labeled music library; the system supports manual track switching, loop playback, and GUI interaction. Its key contribution is the first end-to-end, low-latency (mean 1.18 s), cloud-independent emotion–music adaptive closed loop, balancing real-time performance with user controllability. Experimental results show an emotion recognition accuracy of 89.2% and a 41% improvement in self-reported emotional well-being (N = 127), validating the efficacy and feasibility of localized music therapy for real-time emotion regulation.
📝 Abstract
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.