Emotion-Aware Music Recommendations: A Transfer Learning Approach Using Facial Expressions

The influence of music on mood and emotions has been widely studied, highlighting its potential for self-expression and personal delight. As technology continues to advance exponentially, manually selecting and analyzing music from the vast array of artists, songs, and listeners becomes impractical. In this study, we propose a system called the “emotion-aware music recommendations” that leverages real-time facial expressions to determine a person’s emotional state. Deep learning models are employed to accurately detect facial emotions, leveraging the principles of transfer learning. By combining the model’s output with the mapped songs from the dataset, a personalized playlist is created. The main objective of the study is to effectively classify user emotions into six distinct categories using pre-trained models. Experimental studies conducted on the proposed approach employ the RAF-ML benchmark facial expression dataset. The findings indicate that the model outperforms existing approaches, demonstrating its effectiveness in generating tailored music recommendations.