Music implication and suggestion system using emotion detection.

A recommendation system is the building block of massive enormous systems that use specific recommender algorithms to recommend a single item or a group of items to users based on such forecasts. In this instance, a Music. People often get confused while having to choose from a wide range of collection of songs. There are many algorithms available for music, dining, shopping, to solve this problem. The goal of this algorithm is to provide appropriate recommendations to the users based on personalized preferences and current mood. The algorithm will also analyse the current emotion of the user. Analysis of facial expressions of the user will help the algorithm to suggest the music accurately based on what the user wants to hear at the time. Music recommendations have a wide scope to suggest content to the user based on their inclination, recorded liking and current mental state. Humans make use of their facial expressions more than anything else to express their thoughts and emotions. Many users agree to the fact that often they are unable to figure out which content to listen because of the vast data present around. Developing such algorithm that would help the user decide which song to listen will help lower the stress level. The user will not have to search for songs, since the algorithm would automatically choose the best matching songs based on the state of mind of the user. The algorithm would also curate playlist based on the liking and emotion of the user, thereby saving the time and efforts of the user to find a playlist by themselves. Here, user reliant and user independent datasets can be created, and face emotions can be recorded using an integrated camera. [ABSTRACT FROM AUTHOR]