Abstract

In the rapidly evolving realm of personalized digital interactions, the convergence of emotion detection via facial expression analysis and music recommendation systems offers a unique avenue to enrich user satisfaction and involvement. This survey paper delves into the emerging domain of tailoring music playlists based on facial expressions, which blends advancements in music information retrieval, emotional computing, and computer vision. Through a systematic review of existing literature, we identify the primary methodologies, technologies, and algorithms utilized for real-time facial expression recognition and their fusion with music recommendation engines to customize musical experiences according to listeners' emotional states. We examine the theoretical foundations of the correlation between emotion and music, the difficulties in accurately mapping intricate facial expressions to specific emotional states, and the subsequent selection of music tracks that resonate with or aim to influence these emotions. By offering a comprehensive overview of current progress, identifying research gaps, and outline directions for future work in creating empathetic and responsive music playlist customization platforms.