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Related Experiment Video

Updated: Oct 1, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

A real time face mask detection system using convolutional neural network.

Hiten Goyal¹, Karanveer Sidana¹, Charanjeet Singh¹

¹Department of Computer Science and Engineering, Maharaja Ranjit Singh Punjab Technical University, Bathinda, India.

Multimedia Tools and Applications

|March 2, 2022

Summary

This summary is machine-generated.

This study presents an automated face mask detection model to enforce public mask-wearing during the COVID-19 pandemic. The model achieved 98% accuracy, offering an efficient solution for public spaces.

Keywords:

COVID-19 Convolutional neural network (CNN)Deep learning OpenCV Real-time face mask detection

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Last Updated: Oct 1, 2025

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computer Vision
Public Health Technology

Background:

The COVID-19 pandemic necessitated public health measures like mandatory face mask usage.
Automated systems are crucial for enforcing face mask policies efficiently in public areas.

Purpose of the Study:

To develop and evaluate an automated face mask detection model for static images and real-time videos.
To assess the model's performance against established deep learning architectures.

Main Methods:

A face mask detection model was trained and evaluated on a Kaggle dataset of approximately 4,000 images.
The model classifies images into 'with mask' and 'without mask' categories.

Main Results:

The proposed model achieved a performance accuracy rate of 98%.
It demonstrated superior computational efficiency and precision compared to DenseNet-121, MobileNet-V2, VGG-19, and Inception-V3.

Conclusions:

The developed face mask detection model is highly accurate and efficient.
This technology can be implemented as a digital scanning tool in various public and commercial locations to aid in health policy enforcement.