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https://github.com/sloweyyy/facial-expression-recognition-through-portrait-images
https://github.com/sloweyyy/facial-expression-recognition-through-portrait-images
Last synced: 15 days ago
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- Host: GitHub
- URL: https://github.com/sloweyyy/facial-expression-recognition-through-portrait-images
- Owner: sloweyyy
- License: mit
- Created: 2024-05-16T19:17:21.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-17T14:56:41.000Z (6 months ago)
- Last Synced: 2024-12-07T21:48:50.285Z (16 days ago)
- Language: Jupyter Notebook
- Size: 11.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Facial Expression Recognition System
This repository contains a Python-based implementation of a facial emotion recognition system, designed to classify emotions from portrait images.
## Paper
[Facial Emotion Recognition through Portrait Images](https://www.researchgate.net/publication/380533775_Facial_Emotion_Recognition_through_Portrait_Images)
## Overview
Facial expression recognition is a crucial aspect of human-computer interaction, with applications in various fields like psychology, marketing, robotics, and virtual assistants. This project explores the performance of traditional machine learning models for classifying five basic emotions: anger, sadness, fear, happiness, and neutral.
## Key Features
- **Traditional ML Models:** The project implements Support Vector Machines (SVM), Random Forest, and Decision Tree with Gini Index.
- **Feature Extraction:** Utilizes Histogram of Oriented Gradients (HOG) for extracting facial features.
- **Datasets:** Employs AffectNet and Flickr Faces HQ datasets for training and evaluation.
- **Performance Evaluation:** Provides accuracy, precision, recall, and F1-score metrics for each emotion class.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
The following resources were used in the development of this project:
- [Facial expressions training data](https://www.kaggle.com/datasets/noamsegal/affectnet-training-data) - Kaggle
- [Flickr-Faces-HQ Dataset (FFHQ)](https://www.kaggle.com/datasets/arnaud58/flickrfaceshq-dataset-ffhq) - Kaggle
- [Decision Tree-Based Federated Learning: A Survey](https://doi.org/10.3390/blockchains2010003) - Wang, Z.; Gai, K.
- [Exploring the intricacies of random forest in machine learning](https://medium.com/data-analytics-magazine/exploring-the-intricacies-of-random-forest-in-machine-learning-4ee23ad465dc) - Peters, M.
- [Machine Learning Algorithms(16) — Support Vector Machine (SVM)](https://medium.com/towardsdev/machine-learning-algorithms-16-support-vector-machine-svm-878c2e1d024f) - Dissanayake, K.
- [Facial expression recognition based on random forest and convolutional neural network](https://doi.org/10.3390/info10120375) - Wang, Y., Li, Y., Song, Y. H., \& Rong, X.
- [Biometrics recognition using deep learning: A survey](https://doi.org/10.1016/j.patcog.2021.108245) - Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M., \& Zhang, D.
- [Facial expression recognition via learning deep sparse autoencoders](https://doi.org/10.1016/j.neucom.2017.08.043) - Zeng, N., Zhang, H., Song, B., Liu, W., Li, Y., \& Dobaie, A. M.
## Author
Truong Le Vinh Phuc
## Contact