COVID 19 Authorized Entry Using Face Mask Detection

The system is designed to detect the faces and to determine whether the person wears a face mask or not. Using the above data, we can decide whether the concerned person can be allowed inside public places such as the market, or a hospital.

AI & Machine LearningJupyter NotebookMIT

Abstract

COVID 19 Authorized Entry Using Face Mask Detection is an open-source AI & Machine Learning project. The system is designed to detect the faces and to determine whether the person wears a face mask or not. Using the above data, we can decide whether the concerned person can be allowed inside public places such as the market, or a hospital. It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

The system is designed to detect the faces and to determine whether the person wears a face mask or not. Using the above data, we can decide whether the concerned person can be allowed inside public places such as the market, or a hospital.

"Prevention is better than cure" is one of the effective measures to prevent the spreading of COVID-19 and to protect mankind. Many researchers and doctors are working on medication and vaccination for corona.

2. Objective

The system is designed to detect the faces and to determine whether the person wears a face mask or not. Using the above data, we can decide whether the concerned person can be allowed inside public places such as the market, or a hospital.

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Jupyter Notebook
  • NVIDIA Jetson Nano
  • Raspberry Pi Camera v2 or USB Camera
  • Relay breakout board

5. System Requirements

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

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Rahul24-06/COVID-19-Authorized-Entry-using-Face-Mask-Detection.git
cd COVID-19-Authorized-Entry-using-Face-Mask-Detection

Full setup instructions are in the project README.

7. Future Enhancements

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

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Rahul24-06 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.

View Source on GitHubDownload ZIP

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