YouTube Music Recommendation System Based on Face Expression

Abstract

Nowadays face recognition system is widely used in every field of computer vision applications such as Face lock-in smartphones, surveillance, smart attendance system, and driverless car technology. Because of this, the demand for face recognition systems is increasing day by day in the research field. The aim of this project is to develop a system that will recommend music based on facial expressions. The face-recognition system consists of object detection and identifying facial features from input images, and the face recognition system can be made more accurate with the use of convolutional neural networks. Layers of convolution neural network are used for the expression detection and are optimized with Adam to reduce overall loss and improve accuracy. YouTube song playlist recommendation is an application of a face recognition system based on a neural network. We use streamlit-webrtc to design the web frame for the song recommendation system. For face detection, we used the Kaggle-FER2013 dataset, and images in the dataset are classified into seven natural emotions of a person. The system captures the emotional state of a person in real-time and generates a playlist of youtube songs based on that emotion.