Attendance-Management-System-using-Face-Recognition

Overview

The Attendance Management System is a Python-based application that leverages face recognition technology to automate the process of tracking student attendance. This project uses OpenCV for face detection and recognition, along with a user-friendly graphical interface built with Tkinter. It includes features for capturing images for training, real-time face recognition, and managing attendance records efficiently.

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Features

• Face Recognition: Automatically recognize students' faces and mark their attendance.
• Image Capture: Capture and save images for training the recognition model.
• Manual Attendance: Option to manually fill attendance records.
• CSV Export: Generate attendance reports in CSV format.
• Database Integration: Store attendance records in a MySQL database.

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Technologies Used

• Python
• OpenCV
• Tkinter
• NumPy
• Pandas
• MySQL
• Pillow

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Installation

1. Clone the Repository

git clone https://github.com/ptrishita/Attendance-Management-System-using-Face-Recognition.git
cd Attendance-Management-System-using-Face-Recognition

2. Install Required Packages

Ensure Python is installed on your system, then install the necessary dependencies:

pip install -r requirements.txt

3. Set Up MySQL Database

• Create a MySQL database to store attendance records.
• Update the database connection details in the code as needed.

4. Download Haarcascade

Download the Haarcascade XML file for face detection from the OpenCV GitHub repository and place it in the project directory.

5. Create a 'TrainingImage' Folder

Before running the main_app.py, make sure to create a 'TrainingImage' folder in the project directory. This folder will store the captured images used for training the face recognition model.

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Usage

1. Capture Images

• Run main_app.py to open the GUI.
• Enter the student's enrollment number and name.
• Click on "Take Images" to capture their face images.

2. Train the Model

• After capturing images, click on "Train Images" to train the face recognition model.

3. Automatic Attendance

• Select "Automatic Attendance" to start the face recognition process using the webcam.

4. Manual Attendance

• Use the "Manually Fill Attendance" option to manually fill attendance records.

5. View Registered Students

• Access the admin panel to view the list of registered students and their details.

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Directory Structure

Attendance Management System using Face Recognition/
│
├── Attendance/                              # Directory to save attendance records
  ├── Manually Attendance/                   # Directory to save manual attendance records
├── StudentDetails/                          # Directory to save student details CSV
├── TrainingImage/                           # Directory to store training images
├── TrainingImageLabel/                      # Directory to save the trained model
├── haarcascade_frontalface_default.xml      # Haarcascade file for face detection
├── haarcascade_frontalface_alt.xml          # Haarcascade alternative file for face detection
├── requirements.txt                         # Required Python packages
├── main_app.py                              # Main application file
├── mini_app.py                              # Simple GUI application for capturing images
├── train_app.py                             # Script for training the face recognition model
├── test_app.py                              # Script for testing face recognition
└── README.md                                # Project documentation

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Contributing

Contributions are welcome! If you have suggestions for improvements or additional features, feel free to fork the repository and submit a pull request.

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License

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

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Acknowledgments

Special thanks to the following:

• OpenCV for face detection and recognition functionalities.
• Tkinter for GUI development.
• NumPy and Pandas for data manipulation and analysis.
• MySQL for database management.

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For any questions or issues, feel free to contact trishitapaul5@gmail.com.