Face Recognition Based Attendance Management System – A Complete Python Project

Face Recognition Based Attendance Management System

Overview

A simple project based on a Face Recognition Attendance System which is developed in Python using OpenCV and Tkinter. The main aim of this project is to automate the attendance process by recognizing faces, so teachers or admins don’t need to call out names manually. It is a smart and efficient solution that saves time and reduces errors in maintaining attendance records.

This project is beginner-friendly yet practical and can be used for schools, colleges, coaching centers, or small offices. With its simple GUI, users can register new faces, train the model, and start taking attendance instantly. The attendance is stored in a CSV/Excel file, making it easy to track and manage.

Project Description

The Face Recognition Attendance System works in three easy steps:

1. Register Face – Capture and store face images of each user.

2. Train Model – Use the stored images to train a recognition model with OpenCV.

3. Take Attendance – Start the attendance session, and the system will recognize faces in real time and automatically mark them present.

This process is simple but effective, ensuring smooth operation without requiring complex setup.

Project Summary

Download New Real Time Projects :-Click here

Code Requirements

Before running the project, ensure the following Python libraries are installed:

• OpenCV (pip install opencv-python)
• Tkinter (built-in with Python)
• Pillow (pip install Pillow)
• Pandas (pip install pandas)

Additionally, if database integration is required, set up WAMP Server and configure MySQL.

Project Structure

The project is structured with clarity to allow easy navigation and setup:

• AMS_Run.py: Main application file with the GUI
• TrainingImage/: Stores user face images (automatically created)
• TrainingImageLabel/Trainner.yml: Trained face data
• StudentDetails/StudentDetails.csv: Contains user records
• Attendance/: Stores attendance logs
• haarcascade_frontalface_default.xml: Face detection model

Functional Flow

1. Image Capture:
   Users enter their ID and name, then click on Take Images. The system captures 200 facial images and stores them in TrainingImage/.
2. Model Training:
   After collecting data, clicking Train Image trains the face recognizer using OpenCV’s LBPH algorithm. This creates the trained model in TrainingImageLabel/.
3. Automatic Attendance:
   Clicking on Automatic Attendance activates the camera, recognizes faces in real time, and marks attendance into a .csv file based on time and subject.
4. Manual Attendance Option:
   A button is available for Manually Fill Attendance to record attendance without facial recognition.
5. Database Integration (Optional):
   Attendance records can be stored in a MySQL database by configuring the DB name in AMS_Run.py and using WAMP Server.

Available Features

The Face Recognition Attendance System comes with several powerful yet easy-to-use features that make it a complete attendance management solution:

1. Face Data Capture and Storage

The system allows users (students or employees) to register their faces by capturing multiple images through the webcam. These images are stored locally, ensuring that the system has enough samples to recognize the individual later with high accuracy.

2. Real-time Face Recognition and Attendance Logging

Once trained, the system can detect and recognize faces in real-time using OpenCV. When a registered face is matched, the system automatically records attendance along with the date and time, eliminating the need for manual entry.

3. CSV-based Attendance Report Generation

Attendance data is automatically saved into CSV files, which makes it easy to open, view, and manage using applications like MS Excel. Teachers, admins, or employers can quickly check attendance logs without needing a complex database setup.

4. Manual Attendance Support

In case the camera fails to detect a face or if a student/employee needs to be marked manually, the system provides an option to add attendance entries by hand. This ensures accuracy and flexibility in real-world use.

5. Optional MySQL Database Storage

While the system works with CSV files by default, it also includes optional support for MySQL integration. This feature is helpful for larger organizations or institutions where centralized storage and advanced reporting are required.

6. Simple and User-friendly Tkinter GUI

The entire system is built with a clean Tkinter graphical interface, making it easy to use for non-technical people as well. The interface provides options like “Register Face,” “Train Data,” and “Take Attendance” in an intuitive layout.

7. Customizable Subject and Session Timing

Admins can configure subject names, classes, or work sessions directly in the system. This ensures that attendance is logged under the correct category, which is useful for academic or workplace reporting.

8. Optimized for Training up to 10 Users

The project is designed as a lightweight solution, suitable for small classes or offices. It can effectively train and recognize up to 10 users, making it a great project for students as well as small-scale practical use.

How to Use

1. Download and extract the ZIP file.
2. Install all required dependencies.
3. Create a TrainingImage folder in the root directory if not already present.
4. Open AMS_Run.py and configure your system paths and database credentials (if needed).
5. Run AMS_Run.py and use the UI for registration, training, and attendance.

We have projects Available in all languages:–Click Here