Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Perception and Practice Towards Hand Hygiene during Pandemic among Patients Visiting OPD in A Private Institution

One of the most significant and well-publicized prevention practises for Covid 19 is hand cleanliness. Face masks and social withdrawal are useless without good hand hygiene. The healthcare professionals can only intervene and raise awareness to enhance the public's hand hygiene practises after they are aware of the public's perceptions of and barriers to hand hygiene. A private dental facility had 150 outpatients participate in this cross-sectional questionnaire survey. Ten questions addressing various facets of hand hygiene and perceived obstacles made up the survey. The information from Google Forms was then imported into SPSS Version 15 using Excel. Data were presented as frequencies and percentages after the chi square test, and a p value of 0.05 or less was regarded as statistically significant.. In our study, 92.62 percent of outpatients at a private facility said that they continue to take measures against COVID19. 83.89% of our patients agreed that good hand hygiene habits are crucial for preventing COVID19. Whereas 38.26% of outpatients claimed to only wash their hands for 30 seconds, 33.56% of outpatients claimed to wash their hands for a full minute. In contrast to the 48.32 percent who said hand sanitizer is best and important for hand hygiene, 51.68 percent of outpatients said soap and water is best and essential for hand hygiene. According to the study's findings, the participants had a reasonable understanding of hand hygiene and its significance. Yet, there is a need for greater awareness of the finishing details on touch surfaces. Thus, it is advised that media-based propaganda and awareness campaigns have a positive impact and should be kept up, with a stronger focus on the finer points. © 2023 IEEE.

Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E. © 2022 ACM.

Face Mask Detector for Raspberry Pi Based on Computer Vision and Edge Computing

The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. © 2023 IEEE.

Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%. © 2022 IEEE.

Face Mask And Body Temperature Detection In Sensor Enabled Automated Door Control System

Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.

Bacteria Filtration Efficiency for Different Types of Protective Face Masks

Emerging infectious diseases that we are witnessing in the modern age are among the leading public health concerns. They most often occur in the form of epidemics or pandemics, and they have not been sufficiently researched. Owing to the current coronavirus disease 2019 (COVID-19) pandemic, the World Health Organization has published various recommendations to prevent the spread of this communicable disease, including a recommendation to wear protective facial masks. Therefore, this study aimed to determine the filtration effectiveness of bacteria, yeasts, and molds on three different commonly and commercially available masks used in children's educational institutions. In addition, the bacterial content of indoor air bioaerosols was identified. The genera Staphylococcus and Micrococcus were dominant in all samples, whereas bacteria of the genera Bacillus, Acinetobacter, and Corynebacterium were identified at a significantly smaller number. Bacterial, yeast, and mold filtering effectiveness increased from the single-layer cloth mask, which proved to be the least effective, to the surgical mask, to the filtering facepiece type 2 (FFP2) mask. Furthermore, surveys are needed to study the effectiveness of protective measures.

What is behind the mask? Social representation of mask wearing

Background: During the COVID-19 pandemic, a preventive and widely mandatory use of face masks was a dominant segment of the infection prevention and control of the epidemic. Covering about 60-70% of the facial surface, face masks dramatically affect social interactions-especially emotion recognition, expression and mentalization. Difficulties in communication in the doctor-patient relationship become of paramount importance to the effectiveness of the healing work. This becomes even more critical when the patient suffers from a disorder characterized by a mentalization deficit. In our study, we use the theory of social representations to examine the contents with which mask wearing has become part of our everyday knowledge. Objectives: We aimed to explore the social representations of mask wearing considering its impact on interpersonal communication, in groups where the effectiveness of mutual understanding is critical. Methods: In our study, carried out during the second and third waves of the coronavirus epidemic in Hungary, we gave a free association task to the target word mask-wearing" in a group of medical doctors, and hospitalized somatic and psychiatric patients and healthy controls (total of 81 subjects, mean age 43.1 [13.83] years), then used the obtained associations to form semantic categories and to map the structure of social representations within the groups using a rank-frequency method. Results: The positive experience of safety and the negative experience of physiological discomfort caused by the facemasks were consistently central to the social representations of mask-wearing in all study groups. Differences were found between groups in terms of more mature elaborative categories, as well as anxiety, aggression, helplessness, damaged dependency needs, and forced conformity. Conclusions: The analysis of the social representations revealed ambivalent meanings of the mask wearing. Although there were significant differences in the structure of mask-related social representations, the mask was recognized as an "inconvenient but necessary" health protection measure in most of the groups studied. Based on the results, each group may be at risk in a different way or deal differently with the pandemic based on their specific representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Hungarian) Elmeleti hatter: A COVID-19-pandemia idejen a jarvanyugyi intezkedesek meghatarozo reszeve valt az arcmaszkok viselesenek preventiv es szeles koru alkalmazasa. Az arcmaszkok az arcfelulet mintegy 60-70%-at lefedve jelentosen befolyasoljak a szocialis interakciokat - kulonosen az erzelemfelismerest, erzelemkifejezest es mentalizalast. A kommunikacioban fellepo nehezsegek a gyogyito munka hatekonysaga szempontjabol kiemelt jelentoseguve valnak az orvos-beteg kapcsolatban. Ennek meg kritikusabb esetei azok a helyzetek, amikor a paciens mentalizacios deficittel jellemezheto zavarban szenved. Tanulmanyunkban a szocialis reprezentaciok elmeletet hasznaljuk annak vizsgalatara, hogy a maszkviseles milyen tartalmakkal valt a kozos tudas reszeve. Celkituzes: Vizsgalatunkban a maszkviseles szocialis reprezentaciojanak felterkepezeset tuztuk ki celul, figyelembe veve annak interperszonalis kommunikaciora gyakorolt hatasat, olyan csoportokban, ahol a kolcsonos megertes hatekonysaga kiemelt jelentoseggel bir. Modszerek: Kutatasunkban a koronavirus-jarvany masodik es harmadik magyarorszagi hullama idejen, orvos, szomatikus es pszichiatriai beteg csoportban, valamint kontrollcsoportban (osszesen 81 fo, atlageletkor 43,1 [SD = 13,83] ev) szabad asszociacios feladatot adtunk a maszkviseles" hivoszora. A nyert adatokbol szemantikus kategoriakat kepeztunk, majd ranggyakorisag-eljarassal felterkepeztuk a szocialis reprezentaciok szerkezetet az egyes csoportokon belul. Eredmenyek: A vizsgalati csoportok maszkhasznalathoz kapcsolodo szocialis reprezentaciojaban egysegesen kozponti elemkent jelent meg a maszkviseles altal nyujtott biztonsagelmeny, valamint a maszk zavaro testerzetet kelto hatasa. Kulonbseget talaltunk az egyes csoportok kozott elaborativ kategoriak megjelenese, illetve szorongas, agresszio, tehetetlenseg, serult dependenciaszukseglet, valamint a kenyszeru alkalmazkodas tekinteteben. Kovetkeztetesek: A maszkviseles szocialis reprezentaciojanak elemzese alapjan a maszkviseles ambivalens jelentestartalmakat hordoz. Bar a maszkviseleshez kapcsolodo szocialis reprezentaciok strukturajaban szamottevo kulonbsegek is mutatkoztak, ugyanakkor a legtobb vizsgalt csoportban a maszk a virusvedelem szempontjabol kenyelmetlen, de szukseges" eszkozkent kerult felismeresre. Az eredmenyek alapjan az egyes csoportok sajatos reprezentacioik alapjan eltero modokon lehetnek veszelyeztetettek, illetve kuzdhetnek meg a pandemia idejen kialakult helyzettel. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Detection of Real Time Face Mask using Convolutional Neural Network

In recent years, the global pandemic like COVID - 19 has changed the lifestyle of people. Wearing face mask is must in order to stay safe and healthy. This paper presents a real-time face mask detector which identifies whether a human is wearing a mask or not. Moreover, this system can also recognize the person wearing a face mask inappropriately or wear other things except a face mask. The proposed algorithm for face mask detection in this system utilizes Haar cascade classifier to detect the face and Convolutional Neural Networks to detect the mask. The whole system has been demonstrated in a practical application for checking people wearing face mask. © 2023 IEEE.

Security Vulnerability in Face Mask Monitoring System

The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability. © 2023 Bharati Vidyapeeth, New Delhi.

Computerized Face Mask Detection System Using Deep CNN and Transfer Learning

Since the COVID-19 pandemic is on the rise again with hazardous effects in China, it has become very crucial for global individuals and the authorities to avoid spreading of the virus. This research aims to identify algorithms with high accuracy and moderate computing complexity at the same time (although conventional machine learning works on low computation power, we have rather used CNN for our research work as the accuracy of CNN is drastically greater than the former), to identify the proper enforcement of face masks. In order to find the best Neural Network architecture we used many deep CNN Methodologies to solve classification problem in regards of masked and non masked image dataset. In this approach we applied different model architectures, like VGG16, Resnet50, Resnet101 and VGG19, on a large dataset to train on and compared the model on the basis of accuracy in which VGG16 came out to be the best. VGG16 was further tuned with different optimizers to determine the one best fit of the model. VGG16 gave an ideal accuracy of 99.37% with the best fit optimizer over a real life data set. © 2023 IEEE.

Masked Face Recognition Using Multi-Task Cascaded Convolutional Networks and FaceNet

Face masks have been widely used since the start of the COVID-19 pandemic. Facial detection and recognition technologies, such as the iPhone's Face ID, heavily rely on seeing the facial features that are now obscured due to wearing a face mask. Currently, the only way to utilize Face ID with a mask on is by having an Apple Watch as well. As such, this paper intends to find initial means of a reliable personal facial recognition system while the user is wearing a face mask without having the need for an Apple Watch. This may also be applicable to other security systems or measures. Through the use of Multi-Task Cascaded Convolutional Networks or MTCNN, a type of neural network which identifies faces and facial landmarks, and FaceNet, a deep neural network utilized for deriving features from a picture of a face, the masked face of the user could be identified and more importantly be recognized. Utilizing MTCNN, detecting the masked faces and automatically cropping them from the raw images are done. The learning phase then takes place wherein the exposed facial features are given emphasis while the masks themselves are excluded as a factor in recognition. Data in the form of images are acquired from taking multiple pictures of a certain individual's face as well as from repositories online for other people's faces. Images used are taken in various settings or modes such as different lighting levels, facial angles, head angles, colors and designs of face masks, and the presence or absence of glasses. The goal is to recognize whether it is the certain individual or not in the image. The training accuracy is 99.966% while the test accuracy is 99.921%. © 2022 IEEE.

YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.

An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

Longitudinal Analysis of Mask and No-Mask on Child Face Recognition

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-The-shelf (COTS) system under child cross-Age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease by approximately , , , and , respectively. © 2022 ACM.

In Real Time Face Mask Detection Alert System using CNN Model

Today's current scenario of the coronavirus pandemic (Covid19), where in the future there will be a need for efficient applications of real-time mask detection. Because, nowadays it is very difficult for doctors to handle patients infected with corona virus. Our major purpose of building a face-mask detection alert system using OpenCV that can detect individual person's if he/she is wearing a face mask or not wearing a face-mask using CCTV Camera, with quite a good accuracy. And also building and training the Convolutional Neural Network (CNN) using keras framework. After that, He / She refused to go to the locations or the regions wherever the officials were strictly asked to wear face-mask. After denying way in to the individual, the officers or the authorized person will receive an email in real time where the photograph of the person can be attached. In away screen panels could be installed at the entrances where the person's denied access can see a pop-up warning message. Where he/she would be advised to wear a face mask before getting access. This type of face mask detection alert system has some applications in schools, colleges, malls, theaters, offices and also other major crowded places or areas where it expects large public gathering. © 2022 IEEE.

3D-Printed Face Mask with Integrated Sensors as Protective and Monitoring Tool

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

A Review of 2020 State and DC Face Mask Guidelines for U.S. Healthcare Workers during the Covid-19 Pandemic.

This study reviewed state and District of Columbia (DC) health department guidelines for the use of face masks by healthcare workers during the COVID-19 pandemic via an October 2020 internet search and compared these guidelines to those from the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC). Guidelines varied between states and DC with respect to N95 face mask and surgical mask use, as well as to extended use and re-use of N95 masks. Uniform guidance based on emerging evidence should be required for creating policy and procedures for healthcare workers during this and future pandemics.