Face mask detection in COVID-19: a strategic review

Vibhuti,; Jindal, Neeru; Singh, Harpreet; Rana, Prashant Singh

doi:10.1007/s11042-022-12999-6

Cited by 28 publications

(17 citation statements)

References 104 publications

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“…Face mask detection is a technology that determines the likelihood of individuals wearing face masks in a specific area. It has become particularly relevant during the COVID-19 pandemic, as face masks are crucial in preventing virus transmission [4]. Face mask detection typically involves the integration of cameras and AI algorithms to analyze facial features and identify whether someone is wearing a mask [4].…”

Section: A Backgroundmentioning

confidence: 99%

“…It has become particularly relevant during the COVID-19 pandemic, as face masks are crucial in preventing virus transmission [4]. Face mask detection typically involves the integration of cameras and AI algorithms to analyze facial features and identify whether someone is wearing a mask [4]. This technology is used in airports, hospitals, schools, and corporations for purposes such as enforcing maskwearing laws and monitoring compliance.…”

Section: A Backgroundmentioning

confidence: 99%

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RFID and Facemask Detector Attendance Monitoring System

Wong,

Lee,

Sim

2023

IJORAS

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The article emphasizes the significance of attendance monitoring for safety during the COVID-19 pandemic and proposes an RFID-based solution coupled with face mask detection systems to address attendance challenges. The project aims to create a contactless monitoring system that ensures face mask compliance and provides real-time attendance data for data-driven decision-making. The article also covers various technology-related topics, including the historical usage of face masks, the development of attendance systems using biometric identification and electronic methods, and facial recognition technology's applications in surveillance and finance. It introduces XAMPP, a user-friendly web application development and testing tool, and presents an overview of the IC7408 chip used in digital electronics. The study's key findings show that increasing sample size and optimizing epochs and batch size improve face mask detection accuracy, while RFID scanner distance affects scanning delay and accuracy. The research provides valuable insights into the performance of the proposed attendance monitoring system.

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Section: A Backgroundmentioning

confidence: 99%

Section: A Backgroundmentioning

confidence: 99%

RFID and Facemask Detector Attendance Monitoring System

Wong,

Lee,

Sim

2023

IJORAS

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“…The use of personal protective equipment (PPE), disinfectants, and sanitizers is critical in this situation for the appropriate containment of the disease [ 95 ]. Researchers at the Massachusetts Institute of Technology (MIT) employed a freeze-dried cellular machinery technique to develop nanosensors for use in paper diagnostics embedded in a face mask for viruses, such as Ebola and Zika [ 96 , 97 ].…”

Section: Synergy Between Covid-19 Prevention Detection and Diagnosis ...mentioning

confidence: 99%

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure

et al. 2022

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The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic’s short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic’s aftereffects, utilising the notion of the bullwhip phenomenon’s short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

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“…Similarly, in [ 20 ], they use technologies for mask identification, analyzing in real time the category to which it belongs, classifying into two classes Mask and NoMask, adding methods to improve the dataset, and eliminating images with low light.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Mask Classification with a New Convolutional Neural Model and Its Real-Time Implementation

Campos

Melín

Sánchez

2023

Life

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The world has been greatly affected by the COVID-19 pandemic, causing people to remain isolated and decreasing the interaction between people. Accordingly, various measures have been taken to continue with a new normal way of life, which is why there is a need to implement the use of technologies and systems to decrease the spread of the virus. This research proposes a real-time system to identify the region of the face using preprocessing techniques and then classify the people who are using the mask, through a new convolutional neural network (CNN) model. The approach considers three different classes, assigning a different color to identify the corresponding class: green for persons using the mask correctly, yellow when used incorrectly, and red when people do not have a mask. This study validates that CNN models can be very effective in carrying out these types of tasks, identifying faces, and classifying them according to the class. The real-time system is developed using a Raspberry Pi 4, which can be used for the monitoring and alarm of humans who do not use the mask. This study mainly benefits society by decreasing the spread of the virus between people. The proposed model achieves 99.69% accuracy with the MaskedFace-Net dataset, which is very good when compared to other works in the current literature.

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Face mask detection in COVID-19: a strategic review

Cited by 28 publications

References 104 publications

RFID and Facemask Detector Attendance Monitoring System

RFID and Facemask Detector Attendance Monitoring System

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure

Multiclass Mask Classification with a New Convolutional Neural Model and Its Real-Time Implementation

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