Welcome to the Face Attendance System App! This Python-based application utilizes advanced facial recognition technology powered by InsightFace and features an intuitive, web-based interface developed with Streamlit. Created by Harsh Vaidya, this project aims to provide a seamless and efficient solution for managing attendance in various settings like classrooms, offices, and more.
- Real-Time Facial Recognition: Leverages the robust InsightFace library for accurate and efficient recognition.
- Interactive Streamlit Interface: A user-friendly web-based interface for easy interaction.
- Redis Integration: Supports fast data storage and retrieval for real-time performance.
- Attendance History Management: Maintains a detailed log of attendance records with timestamps.
- Customizable Models: Easily replace or upgrade the facial recognition model as needed.
- Multi-User Support: Allows multiple users to access the system simultaneously without performance degradation.
- Role-Based Access Control: Implement different access levels (e.g., Admin, User) for enhanced security.
- Cross-Platform Compatibility: Accessible from any device with a web browser.
- Email Notifications: Automatically sends attendance reports to predefined email addresses.
- Exportable Data: Supports exporting attendance logs to CSV or Excel for further analysis.
- Mobile-Friendly UI: Optimized for both desktop and mobile devices.
- Dark Mode: Toggle between light and dark themes for better user experience.
Follow these steps to set up and run the application on your local machine:
git clone https://github.com/harsh432004/attendance-system-app
cd attendance-system-app
pip install -r requirements.txt
streamlit run app.py
Open your web browser and visit: http://localhost:8501 For a live demo, visit the deployed version at: 13.60.199.34
To contribute effectively, follow these detailed setup instructions:
Follow the steps below to set up and run the project locally:
Clone the repository to your local machine using the following command: git clone https://github.com/your-username/attendance-system-app
Move to the project folder: cd attendance-system-app
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt pip install insightface
Download the buffalo_l and buffalo_sc models from the official InsightFace repository. Place the downloaded models in the appropriate directory within the project.
Install and configure a Redis instance. You can use either Redis Cloud or install Redis locally on your machine.
streamlit run app.py
We welcome contributions to enhance this project! Here's how you can get involved:
- Click the "Fork" button at the top-right corner of the repository.
git checkout -b feature-name
Implement your changes and test thoroughly.
git commit -m "Description of changes"
git push origin feature-name
Submit a pull request with a clear description of your changes.
- Ensure code adheres to the project's coding standards.
- Test changes locally before submitting.
- Provide comments and documentation for new features.
To deploy the project in a production environment, follow these steps:
Build the Docker image for the project with the following command: docker build -t attendance-system-app .
Run the container with the following command: docker run -p 8501:8501 attendance-system-app Access the application at http://localhost:8501.
We are committed to creating a welcoming and inclusive environment for all contributors. By participating in this project, you agree to adhere to the following guidelines:
- Respectful Communication: Be respectful and courteous to others. Harassment or discriminatory behavior will not be tolerated.
- Constructive Feedback: Provide constructive feedback and be open to receiving it.
- Inclusive Environment: Embrace diversity and encourage everyone to contribute, regardless of background or experience level.
- Collaboration: Collaborate with others to solve problems, share knowledge, and improve the project.
- Stay Professional: Keep discussions relevant to the project and refrain from inappropriate content.
This project is perfect for college projects and hackathons and demonstrates the practical application of advanced technologies like facial recognition.
- Harsh Vaidya - Lead Developer
Want to see your name here? Contribute to the project!
For inquiries, issues, or support:
- Harsh Vaidya
- 📧 Email: [email protected]
- Alternatively, create an issue in the GitHub repository for bugs, feature requests, or questions.
Check out the live demo of the application at: 13.60.199.34
- QR Code Attendance: Generate and scan QR codes for instant attendance marking.
- Customizable Alerts: Set notifications for late arrivals or absentees.
- Language Support: Multi-language support for diverse user bases.
- Graphical Insights: View attendance trends and statistics with interactive charts.
- API Integration: Integrate with third-party systems for extended functionality.
- Custom Reports: Generate attendance summaries and detailed reports on demand.
- AI-Powered Insights: Use machine learning to predict attendance trends and identify anomalies.
This project is licensed under the MIT License. See the LICENSE file for details.