Face Mask Detector

Real time face-mask detection using Deep Learning and OpenCV

AI & Machine LearningPythonMIT

Abstract

Face Mask Detector is an open-source AI & Machine Learning project. Real time face-mask detection using Deep Learning and OpenCV. This project uses a Deep Neural Network, more specifically a Convolutional Neural Network, to differentiate between images of people with and without masks. The CNN manages to get an accuracy of 98.2% on the training set and 97.3% on the test set. It is built using Python, OpenCV, Computer Vision, Deep Learning. 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

This project uses a Deep Neural Network, more specifically a Convolutional Neural Network, to differentiate between images of people with and without masks. The CNN manages to get an accuracy of 98.2% on the training set and 97.3% on the test set. Then the stored weights of this CNN are used to classify as mask or no mask, in real time, using OpenCV. With the webcam capturing the video, the frames are preprocessed and and fed to the model to accomplish this task. The model works efficiently with no apparent lag time between wearing/removing mask and display of prediction.

Real time face-mask detection using Deep Learning and OpenCV

2. Objective

Real time face-mask detection using Deep Learning and OpenCV

This project demonstrates how Python, OpenCV, Computer Vision can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonOpenCVComputer VisionDeep Learning

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/Karan-Malik/FaceMaskDetector.git
cd FaceMaskDetector

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 Karan-Malik 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

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship