Automatic Attendance Management System

ROLLCALL an automatic and smart attendance marking and management system which uses Microsoft Azure’s Cognitive service at its core to create a system that could make sure that no human intervention is required and provides government the ability to monitor the attendance of the schools and helps the government officials in mark fake schools.

Full Stack Web DevelopmentPythonMIT

Abstract

Automatic Attendance Management System is an open-source Full Stack Web Development project. ROLLCALL an automatic and smart attendance marking and management system which uses Microsoft Azure’s Cognitive service at its core to create a system that could make sure that no human intervention is required and provides government the ability to monitor the attendance of the schools and helps the government officials in mark fake schools. Attendance is an important part of our education system. Especially in schools in rural areas where the government uses these attendances for their schemes which they introduce to promote education. It is built using Python. Key capabilities include: Student enrolment; Image pre-processing and noise removal; Model training. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Full Stack Web Development mini project or final-year project.

1. Introduction

Attendance is an important part of our education system. Especially in schools in rural areas where the government uses these attendances for their schemes which they introduce to promote education. The daily attendance of the student is used for the ordering of mid-day meals by the government. But any malpractice related to marking the attendance of students results in individuals making a profit at the cost of the future of India. On top of that, the food wastage that happens because of this is problematic. There have been numerous reports regarding the malpractice in attendance of the students.

ROLLCALL an automatic and smart attendance marking and management system which uses Microsoft Azure’s Cognitive service at its core to create a system that could make sure that no human intervention is required and provides the government the ability to monitor the attendance of the schools and helps the government officials in mark fake schools.

2. Objective

ROLLCALL an automatic and smart attendance marking and management system which uses Microsoft Azure’s Cognitive service at its core to create a system that could make sure that no human intervention is required and provides government the ability to monitor the attendance of the schools and helps the government officials in mark fake schools.

This project demonstrates how Python can be applied to a real-world Full Stack Web Development problem.

3. Key Features / Modules

  • Student enrolment
  • Image pre-processing and noise removal
  • Model training
  • Face Detection
  • Database Creation For Attendance
  • Final Report Generation

4. Technology Stack

Python
  • Raspberry pi
  • Raspberry pi camera V2
  • Powerbank ( optional - for electricity issue )

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Swarnimashukla/Automatic-attendance-management-system.git
cd Automatic-attendance-management-system

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Add role-based access control
  • Deploy on a VPS or cPanel hosting with a custom domain
  • Add automated tests and a CI pipeline

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. Explain the frontend, backend and database layers of this application.
  2. How is user authentication and authorisation implemented?
  3. Describe the main database tables / collections and their relationships.
  4. Which REST APIs or routes are exposed and what do they return?
  5. How would you deploy this application to production?

9. Source Code & License

This project is developed by Swarnimashukla and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

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