Face Mask Detection using MobileNetV2

Durga, Garima; Potluri, Haritha; Vinnakota, Amuktha; Prativada, Naga Pavan; Yelavarti, KalyanChakravarti

doi:10.1109/icais53314.2022.9742933

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Cited by 3 publications

References 8 publications

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An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure

Pandey,

Chaturvedi,

Gupta

et al. 2023

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)

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An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure

Pandey,

Chaturvedi,

Gupta

et al. 2023

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)

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Internet of Things Based Face Recognition Using Mobilenetv2 and Facenet Deep Learning Implementations on Raspberry Pi-400

Alshehabi Al-Ani,

Jarullah,

Mohammad

et al. 2024

Preprint

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Face Mask Detection Using OpenCV

Najeeb¹,

Abhishek²,

Tomer³

et al. 2023

IoT, Cloud and Data Science

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As a biosafety precaution, the World Health Organization (WHO) introduced the wearing of face masks after the COVID-19 epidemic. This posed challenges to existing facial recognition systems, so this study was born. In this publication, we describe how to create a system that allows you to identify people from images, even when they wear a mask. The face detector in OpenCV is used in conjunction with Based on the Mobile NetV2 architecture, a classification model in this way, it is possible to determine whether the face is wearing a mask and where it is situated. To conduct face recognition, A Face Net model is used as a feature extractor and a multilayer feedforward perceptron is used for training facial recognition models using a collection of about 4000+ photographs. Of the images, 52.9 percent came with a face mask and 47.1 percent were without mask. The outcomes of the tests demonstration that determining whether or not someone is wearing a mask is 99.65% accurate. Face recognition accuracy for ten people wearing masks is 99.52 percent, whereas face recognition accuracy without masks is 99.96 percent.

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Face Mask Detection using MobileNetV2

Cited by 3 publications

References 8 publications

An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure

An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure

Internet of Things Based Face Recognition Using Mobilenetv2 and Facenet Deep Learning Implementations on Raspberry Pi-400

Face Mask Detection Using OpenCV

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