Face Recognition Attendance System

This project is a Python-based Face Recognition Attendance System designed to streamline the process of recording attendance using facial recognition technology. It leverages OpenCV for face detection and recognition, and MySQL for managing user and attendance data.

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Features

1. User Registration

• Allows the registration of new users with their name.
• Captures 20 facial samples using a webcam.
• Trains the system to recognize the new user.
• Stores user data in a MySQL database.

2. Mark Attendance

• Recognizes faces in real-time using a webcam.
• Links recognized faces to registered users.
• Records attendance with a timestamp in the database.

3. View Attendance Records

• Displays a log of attendance records.
• Fetches data directly from the database.

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Prerequisites

Software Requirements

• Python 3.7 or higher
• MySQL Server

Python Libraries

Install the following Python libraries:

pip install mysql-connector-python opencv-python numpy

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Installation and Setup

1. Clone the Repository

git clone <repo-url>
cd <repo-directory>

2. Configure the Database

• Create a MySQL database named attendance_system.
• Ensure MySQL server is running.
• Update the MySQL connection details in the code (host, user, password, and database) if necessary.

3. Run the Application

Run the script using Python:

python attendance_system.py

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Usage

The system provides an interactive menu with the following options:

1. Register New User

   • Add a new user by entering their name.
   • Capture facial samples and train the system.

2. Mark Attendance

   • Recognize faces and mark attendance for registered users.

3. View Attendance

   • View all attendance records, including names and timestamps.

4. Exit

   • Close the application.

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Project Structure

• Database

  • users table: Stores user information (ID, name, registration date).
  • attendance table: Records attendance data (user ID, check-in time).

• Directories

  • dataset: Stores facial samples (created automatically if not present).
  • recognizer.yml: Stores the trained facial recognition model.

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Notes

• Ensure a functional webcam is connected to your system.
• Facial recognition confidence threshold can be adjusted in the code for better accuracy.
• Attendance marking requires exactly one face to be visible in the frame.

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Contribution

Contributions are welcome! Feel free to:

• Submit issues for bugs or feature requests.
• Create pull requests to improve the system.

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License

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

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Acknowledgements

• OpenCV for providing robust face detection and recognition capabilities.
• MySQL for efficient database management.