YouTube Integrated Personalised Music Recommendations based on Facial Expressions

Music has a profound impact on human emotions, providing a means of expression and a source of comfort. It can reduce stress, evoke various emotions, feelings including joy, relaxation, and motivation. Thus, Music Recommendation systems play a crucial role in suggesting and tailoring music selections based on user preferences. Researchers built models which uses the Local database to provide songs to the users which is limited to a few numbers of songs. In this study a novel model is introduced by incorporating facial expression analysis into music recommendation systems. By this the emotional connection between the listener and the music is deepened. It ensures that the recommended songs align with the listener's current emotional state. These contribute to a more immersive and satisfying user experience. In this model, the image captured using webcam can be used to deduce the emotional state from facial expressions. This can be done by creating a neural network model which is an EHC CNN model. The Model can be further improved by integrating with You tube which can open up to unlimited options to the user. This model can be used to generate recommendations that can capture the user's mood, which can help users reduce the time to find right music.