Face Recognition Attendance Management System

This project is developed for CSE 3200 (Software Development Project II) under the supervision of Mahit Kumar Paul, Assistant Professor, Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology (RUET).

Project Overview

The Face Recognition Attendance Management System utilizes Python and OpenCV to automate attendance marking based on facial recognition. The system employs the Haarcascade Algorithm for face detection and the Local Binary Pattern Histogram (LBPH) for face recognition.

The main functionalities of the system are as follows:

1. Student Registration: Student details (name, roll number, department) are collected and stored in a MySQL database.
2. Image Capturing: Images for registered students are captured and saved in a local directory.
3. Model Training: The model is trained using the captured images for accurate facial recognition.
4. Attendance Marking: When a student’s face is recognized, their name, roll number, and department are displayed, and their attendance is recorded in a .csv file.

Installation

To run this system, ensure you have the necessary dependencies installed:

1. Clone the repository:

   git clone https://github.com/AudityGhosh/Face_Recognition_Attendance_Management_System.git

2. Install the required Python packages:

   pip install opencv-python numpy mysql-connector-python

3. Set up the MySQL database:

   • Create a database to store student information.
   • Ensure that your MySQL server is running.

File Descriptions

• attendance.py: Handles the attendance logic, records attendance in a .csv file.
• developer.py: Contains the backend logic for student data management.
• face_recognition.py: Implements the face recognition algorithm using LBPH.
• haarcascade_frontalface_default.xml: Haarcascade algorithm used for face detection.
• help.py: A helper script for supporting various system operations.
• main.py: The main driver script to start the system.
• student.py: Manages student information and registration.

How to Use

1. Register Students:

   • Run developer.py to add student details into the database.
   • Capture student images using student.py and store them in the local directory.

2. Run the Face Recognition:

   • Execute main.py to launch the system and start recognizing faces.
   • When a student's face is detected, their information is displayed, and their attendance is recorded.

3. Attendance Report:

   • The attendance is recorded in a .csv file where each student’s attendance is updated with each recognition.

License

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

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Acknowledgements

• Instructor: Mahit Kumar Paul, Assistant Professor, RUET
• Libraries Used: OpenCV, MySQL, Numpy
• Face Detection Algorithm: Haarcascade
• Face Recognition Algorithm: Local Binary Pattern Histogram (LBPH)