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Smart Attendance System

The Smart Attendance System is a real-time facial recognition-based application designed for educational institutions and organizations to automate the process of attendance tracking. By leveraging computer vision, the system identifies individuals from a camera feed and records their attendance in a CSV file.

Table of Contents

• Features
• Prerequisites
• Installation
• Configuration
• Usage
• File Structure
• Troubleshooting
• Contributing
• License

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Features

• Real-Time Face Recognition: Detects and recognizes faces using a live camera feed.
• Automated Attendance Logging: Records attendance data (name and timestamp) in a CSV file.
• Session Management: Supports starting and stopping attendance sessions.
• Configurable Settings: Easily configure paths for known faces and attendance logs.
• User-Friendly Interface: Simple GUI built with Tkinter for ease of use.
• Multi-Camera Support: Option to select from multiple connected cameras.
• Security: Basic user authentication to restrict access.
• Error Handling: Graceful handling of exceptions and errors during runtime.

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Prerequisites

Before running the Smart Attendance System, ensure you have the following installed:

• Python 3.x
• pip (Python package manager)

Installation

1. Clone the Repository:

   git clone https://github.com/your-username/smart-attendance-system.git
   cd smart-attendance-system

2. Install the Required Packages:

   Use the following command to install all necessary Python packages:

   pip install -r requirements.txt

3. Set Up Known Faces:

   • Create a directory named known_faces in the project root.
   • Add images of known individuals into this directory. Ensure that the file name of each image corresponds to the person’s name (e.g., john_doe.jpg).

4. Configuration:

   Modify config.ini to set paths for the known faces directory and the attendance CSV file. If not provided, default paths will be used.

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Configuration

The application uses a config.ini file to manage configuration settings. Below is an example configuration:

[Paths]
KNOWN_FACES_DIR = known_faces
ATTENDANCE_CSV_PATH = attendance.csv

[Camera]
CAMERA_INDEX = 0  # Change to 1, 2, etc., if using a different camera

• KNOWN_FACES_DIR: Directory where known faces are stored.
• ATTENDANCE_CSV_PATH: Path to the CSV file where attendance will be recorded.
• CAMERA_INDEX: Index of the camera to be used. Default is 0.

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Usage

1. Start the Application:

   Run the Python script to launch the GUI:

   python attendance_system.py

2. Using the GUI:

   • Start Camera: Click "Start Camera" to begin face detection and recognition.
   • Stop Camera: Click "Stop Camera" to end the session.
   • Attendance Log: The right panel shows a log of recorded attendance.

3. Attendance Log:

   Attendance is automatically saved in the specified CSV file (attendance.csv by default). Each entry includes the name of the recognized individual and the timestamp of attendance.

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File Structure

The project structure is organized as follows:

smart-attendance-system/
│
├── attendance_system.py        # Main application script
├── README.md                   # Project documentation
├── requirements.txt            # Python dependencies
├── config.ini                  # Configuration file
├── LICENSE                     # License information
├── .gitignore                  # Files to be ignored by Git
├── attendance.csv              # CSV file for attendance logs. This is an Example file can be generated by own.

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Troubleshooting

Here are some common issues and solutions:

• Camera Not Working:

  • Ensure that your camera is connected properly.
  • Check the CAMERA_INDEX in the config.ini file and adjust it as necessary.

• Face Not Recognized:

  • Ensure the image in the known_faces directory is clear and well-lit.
  • Try adding more images of the person in different lighting or angles.

• Application Freezes:

  • This might be due to a heavy processing load. Ensure your system meets the requirements and try lowering the camera resolution.

• CSV File Not Generated:

  • Ensure that you have write permissions to the directory where the CSV file is being saved.

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Contributing

Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch: git checkout -b my-feature-branch
3. Make your changes and commit them: git commit -m 'Add my feature'
4. Push to the branch: git push origin my-feature-branch
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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