Abstract
Abstract: Interest in the nexus between music recommendation systems and affective computing has grown recently. This article examines the state of the art in music recommendation systems that use facial expression analysis to improve customer satisfaction. Facial expression detection has been incorporated into computer vision and machine learning systems, opening up new possibilities for personalized music curating. The basic theories and techniques supporting facial expression analysis in the context of emotional computing are first examined in this study. It explores the developments in emotion identification technology, emphasizing important datasets, algorithms, and their advantages and disadvantages. Additionally, this research assesses how well these systems capture user preferences while resolving issues with scalability, accuracy, and privacy. It talks about the effects on user satisfaction and engagement of using facial expressions as a channel for feedback in recommendation systems. In order to improve and develop facial expression-based music recommendation systems, this paper concludes by outlining future directions and possible research routes and highlighting the necessity of multidisciplinary cooperation between computer science, psychology, and musicology. To sum up, this paper offers a thorough analysis of the current state-of-the-art in facial expression-driven music recommendation systems, outlining their innovations, difficulties, and potential directions for growth of multidisciplinary cooperation between computer science, psychology, and musicology
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call