Personalized Music Recommendation System Based on Real-Time Facial Expression Analysis for Enhanced Emotional Experience

Facial expressions are a powerful reflection of human emotions, offering insights into mood and intentions. This research introduces an innovative music recommendation system that leverages facial emotion recognition to enhance user experiences. By employing a custom-trained Convolutional Neural Network (CNN), the system captures and analyzes facial expressions in real time, accurately identifying emotions such as happiness, sadness, or excitement. These detected emotions are then mapped to curated music recommendations, providing users with an emotionally resonant and personalized listening experience. Designed for seamless integration with music streaming platforms, the system incorporates real-time adaptability and user feedback to refine its recommendations over time. Beyond entertainment, this approach holds potential applications in mental health and wellness by offering mood-enhancing music solutions. By addressing the challenges of subjective music selection and overwhelming choices, this system redefines music discovery, fostering a deeper emotional connection between users and their playlists. The study underscores the convergence of artificial intelligence, computer vision, and human-centered design to create a transformative music recommendation experience.