Validating the CorrectWearing of Protection Mask by Taking a Selfie: Design of
a Mobile Application “CheckYourMask” to Limit the Spread of COVID-19

Hammoudi, Karim; Benhabiles, Halim

doi:10.32604/cmes.2020.011663

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…In the tenth paper entitled "Validating the Correct Wearing of Protection Mask by Taking a Selfie: Design of a Mobile Application "CheckYourMask" to Limit the Spread of COVID-19," Hammoudi et al [10] propose a design of a mobile application for permitting everyone having a smartphone and being able to take a picture to verify that his/her protection mask is correctly positioned on his/her face.…”

mentioning

confidence: 99%

Introduction to the Special Issue on Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19

Zhang¹,

QiLong²,

Yuan³

2021

Computer Modeling in Engineering &Amp; Sciences

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The 2019-20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was declared to be a Public Health Emergency of International Concern and recognized as a pandemic by the World Health Organization. The outbreak of COVID-19 has rapidly spread to most countries in the world. This special issue started at 21 April 2020, when there were more than 2.5 million confirmed cases and 174,336 deaths around the world. After 10 months, this special issue closes at 23 February 2021, the two figures increase to 112.2 million confirmed cases and 2.48 million death tolls, respectively.

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confidence: 99%

Introduction to the Special Issue on Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19

Zhang¹,

QiLong²,

Yuan³

2021

Computer Modeling in Engineering &Amp; Sciences

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“…Natural images [32] The MaskedFace-Net [30,31] dataset was used to train and test the K-means classifier that is capable of detection if the face mask is worn correctly or incorrectly. This dataset consists of 137,013 images (as of 5 November 2020) that is based on the Flickr-Faces-HQ (FFHQ) dataset, that was originally created as a benchmark for the generative adversarial networks (GAN) [33].…”

Section: Datasetsmentioning

confidence: 99%

“…The images of people are of good quality, with a size of 256 × 256 px. The images incorporate similar variation as the MaskedFace-Net [30,31] dataset. In this paper, the other classes are treated as one non-person class.…”

Section: Datasetsmentioning

confidence: 99%

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TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

Piątkowski

Walkowiak

2022

Applied Sciences

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As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation.

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“…The COVID-19 contagiousness is considered to be high in comparison to the flu. Hammoudi et al, 2020 presents a mobile application design that allows anybody with a smartphone the ability to snap a picture to verify that his or her protective mask is properly positioned on his or her face [14]. The technique used in this research included Android, OpenCV, and Haarlike.…”

Section: F Validating the Correct Wearing Of Protection Mask By Taking A Selfie: Design Of A Mobile Application 'Checkyourmask' To Limit mentioning

confidence: 99%

Comparison of Convolutional Neural Network Architectures for Face Mask Detection

Yahya¹,

Ramli²,

Nordin³

et al. 2021

IJACSA

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In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A costeffective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely; AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images.

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Validating the CorrectWearing of Protection Mask by Taking a Selfie: Design of a Mobile Application “CheckYourMask” to Limit the Spread of COVID-19

Cited by 13 publications

References 9 publications

Introduction to the Special Issue on Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19

Introduction to the Special Issue on Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19

TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

Comparison of Convolutional Neural Network Architectures for Face Mask Detection

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