Music Recommendation Based on Facial Emotion Recognition
Rajesh B Keerthana V Narayana Darapaneni Anwesh Reddy P
Introduction: Music provides an incredible avenue for individuals to express
their thoughts and emotions, while also serving as a delightful mode of
entertainment for enthusiasts and music lovers. Objectives: This paper presents
a comprehensive approach to enhancing the user experience through the
integration of emotion recognition, music recommendation, and explainable AI
using GRAD-CAM. Methods: The proposed methodology utilizes a ResNet50 model
trained on the Facial Expression Recognition (FER) dataset, consisting of real
images of individuals expressing various emotions. Results: The system achieves
an accuracy of 82% in emotion classification. By leveraging GRAD-CAM, the model
provides explanations for its predictions, allowing users to understand the
reasoning behind the system's recommendations. The model is trained on both FER
and real user datasets, which include labelled facial expressions, and real
images of individuals expressing various emotions. The training process
involves pre-processing the input images, extracting features through
convolutional layers, reasoning with dense layers, and generating emotion
predictions through the output layer. Conclusion: The proposed methodology,
leveraging the Resnet50 model with ROI-based analysis and explainable AI
techniques, offers a robust and interpretable solution for facial emotion
detection paper.