Song Recommendation System Based on Facial Emotion
DOI:
https://doi.org/10.47392/IRJAEM.2024.0197Keywords:
TensorFlow, Streamlit, CNN, OpenCVAbstract
This research is aiming to enhance the user experience in music consumption by incorporating real-time facial emotion analysis. Emotions play a fundamental role in shaping individual preferences, and leveraging facial expressions as a means of understanding user’s emotional states can significantly contribute to personalized music recommendations. Our proposed system begins by capturing real-time facial expressions using a webcam or analyzing static images. These facial expressions are then processed through a CNN-based emotion recognition model trained to classify emotions such as happiness, sadness, anger, and more. The CNN model extracts high-level features from facial images, enabling accurate emotion recognition. Using the detected emotional state as input, our system employs a recommendation algorithm tailored to the user's current emotional state to suggest relevant music or videos from YouTube.
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Research Journal on Advanced Engineering and Management (IRJAEM)
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.