The "Attendance Management System" is a Python-driven application that automates attendance tracking using facial recognition. With OpenCV-python powering face detection and recognition, and SQLite managing the database for user and attendance records, the system ensures reliability and hold good functionality. A user-friendly interface built with Tkinter enhances accessibility.Its features include capturing training images, real-time recognition, and streamlined attendance management.
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Problem Taking attendance manually can be slow, full of mistakes, and not always reliable. In schools, offices, or events, it often causes delays and can be easily misused.
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Challenges Sometimes people forget to mark attendance, or someone might mark it for someone else. This leads to incomplete or incorrect records, causing confusion and problems later.
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Solution The Attendance Management System uses face recognition technology to take attendance quickly and accurately. It scans faces through a camera, identifies the person, and securely saves the attendance in an SQLite database. This saves time, avoids errors, and makes the process smooth and trustworthy.
- User Registration & Login: Secure registration and login with username and password.
- Face Recognition for Attendance: Automatic attendance marking through facial recognition, reducing human intervention.
- Attendance History: Allows users to check their previous attendance logs with date and time.
- Database Integration: All user data and attendance logs are stored in an SQLite database for easy management and retrieval at anytime.
- OpenCV: A computer vision library used to detect and recognize faces in real-time.
- Install with:
pip install opencv-python
- Install with:
- SQLite: A lightweight, self-contained database used to store user details and attendance records.
- Tkinter: Used for creating a simple and interactive graphical user interface (GUI).
- Install with:
pip install tk
- Install with:
- Pillow: A Python Imaging Library (PIL) fork used for processing and handling images (e.g., resizing).
- Install with:
pip install pillow
- Install with:
- NumPy: Provides support for large multi-dimensional arrays and matrices, used with OpenCV for face recognition.
- Install with:
pip install numpy
- Install with:
Follow the steps below to run the project on your local machine:
git clone P2-Attendance_Management_System-Using_Facial_Recognition_AICTE-Internship.git
cd P2-Attendance_Management_System-Using_Facial_Recognition_AICTE-InternshipEnsure Python is installed on your system, then install the necessary dependencies using the command:
pip install -r requirements.txt- Create a SQLite database(here, user.db) to store attendance records.
- Update the database connection details in the code as needed.
Download the Haarcascade XML file for face detection from the OpenCV GitHub repository and place it in the project directory.
- Run
main_gui.pyto open the GUI and it is the main file of the project. - Enter the Username and Password for registration.
- Then Click on Login Button.
- After that , window for marking attendance using facial identification opens.
- Camera opens, and images are captured.
- Click 'ctrl+q' to finish capturing images.
- After capturing images, run
train_model.pyin cmd to train the face recognition model.
- Select
Mark Today's Attendanceto automatically mark attendance for the day, a message can be seen in cmd(terminal).
- Access the
Attendance Historybutton to view the details of attendance.
AttendanceManagementSystem/
├── faces/ # Folder where user images for training are stored
├── trainer.yml # Trained face recognition model file
├── user.db # SQLite database for user data and attendance logs
├── haarcascade_frontalface_default.xml # Haarcascade file for face detection
├── main_gui.py # Main application file that runs the GUI
├── attendance_capture.py # The script that handles capturing attendance using the webcam and the facial recognition model.
├── attendance_create.py # Used for creating new user entries in the system (such as username, password, and storing their face images).
├── train_model.py # Script to train the face recognition model
├── delete_faces.py # Script to delete a user's face data
└── README.md # This guidance file
This version still maintains the friendly and encouraging tone while providing the same clear steps for contributing, feel free to fork the repository and submit a pull request.
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.
- Introduction of the Problem: Describes the problem of manual attendance systems and how it can lead to inefficiency and errors.
- Solution: Shows how the facial recognition system solves this problem with automated attendance marking.
- Technologies and Libraries: Explains the libraries and technologies used, including installation steps.
- Setup Instructions: Guides the user through each step of the installation process.
- Project Structure: Displays the organization of the project files to help the user navigate through them.
- Detailed Working Explanation: Describes how the system functions, from registration to face recognition and attendance logging.
- Contact Information: Provides a way for users to contact you for help.
This README.md follows a clear, detailed, and structured approach, allowing someone unfamiliar with your project to set it up and run it without any problems.
For any questions or issues, feel free to contact [[email protected]].
HAPPY CODING 🎉