Real-Time Face Mask Identification Using Facemasknet Deep Learning Network

Inamdar, Madhura; Mehendale, Ninad

doi:10.2139/ssrn.3663305

Cited by 74 publications

(34 citation statements)

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“…Madhura et al [15] proposed a model called Facemasknet to identify if a person is wearing a facemask properly or not, which summed up to a three-class classification: no mask, improperly worn mask, and with a mask. A customized dataset including 35 images was used to train the model.…”

Section: A Cnn Based Approachesmentioning

confidence: 99%

“…Amit et al [17] developed a two-stage-based detector using two pre-trained CNN models. The first stage of the detector completes the face detection in an image, and the next stage classifies the detected images into a mask and no-mask class, as reported elsewhere [15]. The difference between the two studies was that they used two CNNs for face and mask detection.…”

Section: A Cnn Based Approachesmentioning

confidence: 99%

“…Moreover, facemask detection algorithms have been adopted and trained by existing object detection algorithms, such as, You Only Look Once (YOLO), Single-Shot Detector (SSD), and convolutional neural networks (CNNs) (Briefly discussed in II). Most facemask detection algorithms proposed after the post-Covid-19 world are mainly deep learning (DL)-based algorithms, which is a sub-set of machine learning (ML) algorithms [14], [15], [16], [17], [18]. A DL-based networks include deep neural network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19

et al. 2021

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The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization, wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the "no mask, no service" method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future scope in the related field.INDEX TERMS Covid-19, convolutional neural network, deep neural network, facemask detection, Covid-19 health, object detection, machine learning.

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Section: A Cnn Based Approachesmentioning

confidence: 99%

Section: A Cnn Based Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19

et al. 2021

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“…First CNN extracts the feature and then send to second CNN VGGFace to characterise them with noisy descriptors. A KNN module is to refine such descriptors with respect to it nearest neighbours from huge collections of faces and non faces and jointly performs classification and identify correct facial regions and identify it is with mask or not.Madhura Inamdar [5] proposed facemask net 2D-cnn model with one input layer and 8 convolutional layers. The input layer takes image size in 227*227*3 dimension.…”

Section: Literature Surveymentioning

confidence: 99%

HybridFaceMaskNet: A Novel Face-Mask Detection Framework Using Hybrid Approach

Bhattacharya

2021

Preprint

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Coronavirus disease 2019 (covid-19 ) is a contiguous disease which is caused by severe acute respiratory syndrome coronavirus2(SARAS-2) started from Wuhan, china, and spread all over the world within a few months in 2019. Government of all countries had to apply lockdown to decrease the number of aﬀected patient as mortality rate of many countries became very high at that time. In the awake of 2nd wave of COVID 19 WHO has made mandatory to use mask in largely crowded areas, health centers, communities and in diﬀerent places to prevent spread of virus. Many countries have invented the vacancies but it will ﬁrstly available for corona front line warriors only, not for general people, So, people have to wear mask when they are going out from home. But In recent days it can be followed that people are reluctant to wear mask when they are entering in oﬃces, departmental stores or local shops where, gathering might happen anytime. This could lead to spread of COVID-19 among the communities. With the help of computer-vision, people who are not wearing mask can be detected by generating an alarm signal. To achieve this challenging task, a face mask detector ‘HybridFaceMaskNet’ is proposed, which is a combination of classical Machine Learning and deep learning algorithm. ‘HybridFaceMaskNet’ can achieve state-of-art accuracy on public faces. The real challenges are the low-quality images, diﬀerent distances of people from camera and dynamic lighting on the faces at daylight or in artiﬁcial light.This problem can be overcome by using diﬀerent noise removal techniques. HybridFaceMaskNet is trained with three diﬀerent classiﬁcation of images ‘proper-mask’, ‘incorrect-mask’ and ‘no-mask’ which are collected from real life images and some synthetic data , to generate alarm for diﬀerent scenario .This HybridFaceMaskNet is trained on Google Colab and is compared with diﬀerent existing face mask detector model. There is a possibility of deploy the model in IOT devices as it is light weight compare to other existing models.

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“…This method helps in reducing the waiting time of the passengers at the bus stop. Madhura Inamdar et.al [20] proposed a deep learning-based face mask detection technique which is used to detect the people without face mask at various public places with the accuracy of 98.6%…”

Section: Related Workmentioning

confidence: 99%

Secured College Bus Management System using IoT for Covid-19 Pandemic Situation

Krishnan

Kannan²,

Manikandan

et al. 2021

2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)

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College bus transportation system plays a vital role in students' life. They provide safe and secured journey compared to other mode of transportation. The current COVID-19 pandemic situation has driven the S tate Governments and all the college authorities around India to enforce limitations on transportation and movement of students in order to prevent the spread of disease. But taking the students education into considerations, many state governments have advised to open the colleges with proper precautionary measures. However, student health has to be given more importance as they are the backbone of the country. S o, a S ecured College Bus Management S ystem (S CBMS ) has been introduced which monitors the health of the student and the safety measures followed by the student before entering into the bus. Alert information will be passed to the parent and the college management if a person fails to cross these two stages of screening process. Vehicle location information is passed to the parents periodically in order to prepare their child for the school on scheduled time before the bus reaches their boarding point. College bus attendance is monitored efficiently and updated to college authorities for further actions.

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Real-Time Face Mask Identification Using Facemasknet Deep Learning Network

Cited by 74 publications

References 0 publications

Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19

Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19

HybridFaceMaskNet: A Novel Face-Mask Detection Framework Using Hybrid Approach

Secured College Bus Management System using IoT for Covid-19 Pandemic Situation

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