Music recommendation system using facial expressions

In the age of artificial intelligence and human-computer interaction, personalized music recommendations have become a significant field of study. Traditional music recommendation systems rely heavily on user history, preferences, or manual inputs. However, these approaches often fail to capture the user’s current emotional state, which plays a vital role in determining the type of music a person may want to hear. This project introduces a music recommendation system that leverages facial emotion recognition technology to detect the user’s mood and suggest appropriate songs accordingly. The system uses computer vision for real-time facial expression capture and employs deep learning models for emotion classification. Based on the detected emotion, a music recommendation algorithm retrieves relevant songs from a pre-defined database.