Sentinel Facial Recognition Attendance System

Sentinel is a robust and efficient facial recognition-based attendance system. It leverages advanced image processing techniques and a graphical user interface (GUI) to automate attendance tracking, ensuring accuracy and ease of use for educational institutions, offices, or any organization requiring a secure and reliable attendance solution.

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Features

• Face Detection and Recognition: Uses OpenCV and Haarcascade to detect and recognize faces in real time.
• Automated Attendance Logging: Saves attendance data to an Excel file with timestamps.
• Password Protection: Restricts access to sensitive features through a secure password mechanism.
• Email Notifications: Automatically sends the attendance Excel file via email.
• Graphical User Interface: Intuitive GUI for managing attendance, training data, and settings.
• Multi-user Support: Handles multiple registered users seamlessly.

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Table of Contents

• Installation
• Usage
• Screenshots
• Contributing
• License

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Installation

Follow these steps to set up the project:

1. Clone the repository:

   git clone https://github.com/dev0052/Sentinel-Facial_Recognition_Attendance_System.git

2. Navigate to the project directory:

   cd Sentinel-Facial_Recognition_Attendance_System

3. Install the required dependencies:

   pip install -r requirements.txt

4. Ensure the following files are present:

   • haarcascade_frontalface_default.xml
   • A valid SMTP email configuration in main.py.

5. Run the application:

   python main.py

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Usage

1. Register Users: Use the "Take Images" feature to register users by capturing their face data.
2. Train Model: Train the facial recognition model using the "Train Images" feature.
3. Track Attendance: Start real-time face recognition using the "Track Attendance" feature. Attendance is automatically logged.
4. Email Reports: Attendance files are emailed automatically to the configured recipient.

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Screenshots

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Contributing

Contributions are welcome! If you wish to improve the project:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add your message here"

4. Push the branch:

   git push origin feature-name

5. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Contact

If you encounter issues or have suggestions, feel free to reach out:

• Email: [email protected]
• GitHub: dev0052

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Acknowledgements

• OpenCV for image processing.
• The Python community for amazing libraries and support.