Smart Music Therapy System Using ResNet50V2 Deep Learning Architecture

Abstract

Facial emotion recognition has been recognised as an important aspect of human–computer interaction, enabling computers to understand users' emotional states and interact intelligently with them. This work presents a real-time facial emotion recognition framework coupled with an automated music retrieval system. The system utilises a pre-trained deep residual network based on the ResNet50 model to recognise seven basic facial emotions: anger, contempt, disgust, fear, happiness, sadness, and surprise. Face regions are detected using the Haar Cascade classifier applied to webcam input. The emotional state is used as input to formulate a query to retrieve the appropriate music playlist over the internet. The system has been designed with a cooldown feature that improves the overall user experience by controlling the frequency of music playlist updates. The experimental results show a validation accuracy of 94.46%, thereby demonstrating the system's classification capability