🎓 Face Recognition Attendance System

A comprehensive face recognition-based attendance system built with Python, OpenCV, and Firebase. This system provides an automated solution for student attendance tracking using facial recognition technology with an intuitive graphical user interface.

📷 Screenshots

Main Dashboard

Student Details

✨ Features

• User Authentication: Secure login system with username and password protection
• Student Management: Add, update, delete, and manage student information with Firebase integration
• Photo Capture: Automated facial image capture system for training data collection
• Face Recognition: Real-time face detection and recognition using OpenCV and Haar Cascade classifiers
• Training Module: Train the face recognition model with captured student photos
• Attendance Tracking: Automatic attendance marking with timestamp when a face is recognized
• Attendance Management: View, export, and manage attendance records in CSV format
• Modern GUI: Clean and intuitive user interface built with Tkinter
• Cloud Storage: Firebase integration for secure data storage and management

🛠️ Technologies Used

• Python 3.x: Core programming language
• OpenCV: Computer vision library for face detection and recognition
• Tkinter: GUI framework
• PIL (Pillow): Image processing
• Firebase: Cloud database for student data storage
• NumPy: Numerical computing
• CSV: Attendance record management

📋 Prerequisites

Before running this project, ensure you have the following installed:

Python 3.x
pip (Python package manager)

🔧 Installation

1. Clone the repository

   git clone https://github.com/rohan-benny/Face-Recognition-System.git
   cd Face-Recognition-System

2. Install required packages

   pip install opencv-python opencv-contrib-python
   pip install pillow
   pip install numpy
   pip install firebase-admin

3. Firebase Setup

   • Create a Firebase project at Firebase Console
   • Generate a service account key (JSON file)
   • Rename the file to match the import in firebaseconfig.py or update the filename
   • Place the JSON file in the project root directory
   • Important: Keep this file secure and never commit it to version control

4. Configure Firebase

   • Update the firebaseconfig.py file with your Firebase credentials
   • Ensure your Firebase Realtime Database rules are properly configured

📁 Project Structure

Face-Recognition-System/
│
├── Assets/                          # UI assets and button images
│   ├── background_image.png
│   ├── button_1.png - button_6.png
│   └── ...
│
├── Data/                           # Stored face images for training
│   └── user.[id].[count].jpg
│
├── screenshots/                    # Application screenshots for documentation
│   ├── login.png
│   ├── dashboard.png
│   └── ...
│
├── main.py                         # Main application entry point
├── login.py                        # Login system
├── student.py                      # Student management module
├── train.py                        # Model training module
├── face_recognition.py             # Face recognition module
├── attendance.py                   # Attendance management module
├── firebaseconfig.py               # Firebase configuration
├── haarcascade_frontalface_default.xml  # Haar Cascade classifier
├── classifier.xml                  # Trained face recognition model
├── Attendance.csv                  # Attendance records
└── README.md                       # Project documentation

🚀 Usage

1. Start the Application

   python login.py

2. Login

   • Enter your username and password
   • Default credentials can be set in the login module

3. Student Management

   • Click on "Student Details" button
   • Add new students with their information
   • Capture face samples (150 images per student recommended)

4. Train the Model

   • Click on "Train Data" button
   • Wait for the training process to complete
   • The trained model will be saved as classifier.xml

5. Mark Attendance

   • Click on "Face Recognition" button
   • The system will activate the camera
   • Recognized faces will automatically mark attendance with timestamp

6. View Attendance

   • Click on "Attendance" button
   • View, search, and export attendance records
   • Attendance data is saved in CSV format

📸 Face Capture Guidelines

For best results when capturing student photos:

• Ensure good lighting conditions
• Face should be clearly visible
• Capture from different angles (slightly left, center, slightly right)
• Maintain consistent distance from camera
• Capture 100-150 images per student for better accuracy

🔐 Security Considerations

• Firebase credentials are not included in the repository
• Keep your Firebase service account key secure
• Update login credentials from default values
• Implement proper user authentication for production use
• Regular backup of attendance data is recommended

🐛 Troubleshooting

Camera not working:

• Check if camera permissions are granted
• Ensure no other application is using the camera

Face not detected:

• Improve lighting conditions
• Ensure face is within camera frame
• Check if Haar Cascade file is present

Firebase connection error:

• Verify Firebase credentials
• Check internet connection
• Ensure Firebase database rules allow read/write access

Training errors:

• Ensure Data folder contains captured images
• Verify image file naming convention: user.[id].[count].jpg
• Check if sufficient training data is available

📝 Future Enhancements

• Multi-user role system (Admin, Teacher, Student)
• Email notifications for attendance reports
• Mobile application support
• Advanced analytics and reporting dashboard
• Integration with learning management systems
• Support for multiple camera sources
• Improved face recognition accuracy with deep learning models

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is open source and available under the MIT License.

👤 Author

Rohan Benny

• GitHub: @rohan-benny

🙏 Acknowledgments

• OpenCV community for excellent computer vision tools
• Firebase for cloud storage solutions
• Python community for extensive libraries and support

📞 Support

For support, please open an issue in the GitHub repository or contact the developer.

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Note: This system is designed for educational and small-scale institutional use. For large-scale deployment, consider additional security measures and scalability improvements.