Autonomic Face Mask Detection with Deep Learning: an IoT Application

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdown to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the Sequential Convolutional Neural Network of Deep Learning model to detect the presence of masks correctly without causing over-fitting.

In December 2019, a disease known as COVID-19 outbroke in some part of China which killed a bunch of people in that area. Till Mid-March, the disease spread all over the world killing more people. Due to this, World Health Organization (WHO) had to declare this decease as a pandemic. Therefore, scientists from all over the world were working very hard, giving their best everyday in order to make an anti-dote of this disease but it was expected to take alot of time and it even took. Now, government allowed the citizens to continue their normal way of living but still they made some protocols which was to be followed such as sanitization of hands on a regular interval, maintaining two-yard distance from each other, wearing masks, etc. Considering those protocols, we have evolved a Face-masks Detection System with a view to be useful in figuring out whether or not a person is wearing a mask or not inside the public locations which include Temples, Airports, and so on. The face masks detection database contains a mask and further to the facial snap shots, we've used OpenCV to carry out actual-time face detection from live streaming thru webcam. We have used the database to create a COVID-19 face mask detector from a computer view the use of Python, OpenCV, and Tensor Flow and Cameras. We present an in-intensity reading software that could come across conditions where a face mask isn't always used well. Our software incorporates a two-section configuration of the Convolutional Neural Network (CNN) that could hit upon hidden and unidentified faces and may point-out with pre-hooked up CCTV cameras. This allows in monitoring safety violations, promote using face mask, and ensures a safe running environment.

According to the World Health Organization data, the global epidemic of COVID-19 has profoundly affected the world and has now infected more than eight million people worldwide. The COVID-19 epidemic forced governments worldwide to close their doors to prevent the spread of the virus. Reports indicate that wearing a facemask and maintaining public distance while at work reduces the risk of infection. CCTV cameras are installed in educational and industrial spaces, residential areas, and crowded places. However, it is difficult to physically monitor buildings to find people without masks and people who do not keep public distance, and it is challenging to monitor video footage of large buildings and find people without a facemask and social distance correction. A mixed model that uses in-depth reading and machine learning to face masks and correct public distances will help managers look after people roaming the grounds without hiding their faces and distance from the public. The supervised machine-learning algorithm will train the model with the data available online. The facemask data acquisition set contains masked and non-masked images. OpenCV will be used for real-time face recognition and social distance adjustment from live streaming videos from web-enabled videos. The Convolution Neural Network will be used to extract features from the database. After the feature removal process, the newly installed photo/video will be classified as face masked or face masked. The proposed methodology has shown that person detection, social distance, and F.I. scores approximate 91.2%, and the average F1 score is 90.79%.KeywordsFacemaskSocial distancingCNNOpenCVTensor flowYOLO

The objective is to build an efficient face mask detector using Single Shot Detector (SSD). The algorithm used for face mask detection was a novel SSD and with the comparison of Convolutional Neural Network (CNN). The face mask detection dataset was usedand the ability of the algorithm was measured with the sample size of 136. SSD has achieved accuracy of 92.25% and for CNN it was 82.6%. By using a base architecture of VGG-16, SSD was able to outperform other object detectors like CNN without compromising speed and accuracy. The SSD and CNN are statistically satisfied with the independent sample t-test value (p<0.05) with a confidence level of 95%. Face mask detection using SSD was significantly better accurate than CNN.

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure; dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.

In this paper, we investigate the problem of face mask compliance classification in response to the Corona Virus Disease (COVID-19) pandemic. Since the start of the pandemic, many governments and businesses have been continually updating policies to help slow the spread of the virus, including requiring face masks to use many public and private services. In response to these policies, many researchers have developed new face detection and recognition techniques for masked faces. Many of the developed approaches proposed to address the problem of masked face detection have been relatively successful. However, these approaches almost exclusively focus on detecting the presence or absence of someone wearing a face mask. It is understood and broadly discussed in various reports and the media that there are people not always following the suggested guidelines provided by public health authorities for wearing face masks, which include ensuring the face mask properly covers the nose and mouth. To date, very few research publications exist that investigate the capability of modern classification algorithms to efficiently distinguish between masked faces in the visible band that are either compliant or non-compliant with the suggested guidelines. Furthermore, to the best of our knowledge, there is no publication in the open literature that focuses on the investigation of face mask classification in the thermal band. As thermal sensors continue to improve in quality and decrease in cost, surveillance applications using thermal imagery are expected to continue to grow and benefit those organizations considering the automation of face mask detection and compliance. In this study, we propose an investigation on face mask compliance in both the thermal and visible bands. It is composed of the following salient steps; (1) the creation of a multi-spectral masked face database from subjects wearing or not wearing face masks, (2) the augmentation of the generated database with synthetic face masks to simulate two different levels of non-compliant wearing of face masks, and (3) the assessment of a variety of CNN architectures on the previous augmented database to investigate any differences between classifying thermal and visible masked faces. Experimental results show that face mask compliance classification in both studied bands yield a classification accuracy that reaches 100% for most models studied, when experimenting on frontal face images captured at short distances with adequate illumination.KeywordsFace masksCOVID-19PandemicFace mask classificationDeep learningMask complianceThermal imagingMulti-spectral Imaging

SARS COVID19- virus has been a serious threat to human life, the best way to protect oneself is by wearing a face mask. As per World Health organization (WHO), wearing a mask can cut down the risk of infection by 85%. This study sought to offer an automated solution using several state-of-art Deep Convolution Neural Network (CNN) face recognition models to classify an individual’s face is wearing a mask or not. Determining the same and regulating the same is a difficult task that involves great human efforts. Nine various CNN models are trained, thereafter tested and found that the architecture of NasNet outperforms the other eight state-of-the-art models with respect to accuracy, on both, Real-World Masked Face Dataset (RMFD) and Simulated Masked Face Dataset (SMFD). The proposed approach achieved 100% testing accuracy on SMFD and 99.872% testing accuracy on RMFD.

The usage of mask is necessary for the prevention and control of COVID-19 which is a respiratory disease that passes from person to person by contact and droplets from the respiratory tract. It is an important task to identify people who do not wear face mask in the community. In this study, performance comparison of the automated deep learning based models including the ones that use transfer learning for face mask detection on images was performed. Before training deep models, faces were detected within images using multi-task cascaded convolutional network (MTCNN). Images obtained from face mask detection dataset, COVID face mask detection dataset, mask detection dataset, and with/without mask dataset were used for training and testing the models. Face areas that are detected with MTCNN were used as input for convolutional neural network (CNN), MobileNetV2, VGG16 and ResNet50. VGG16 showed best performance with 97.82% accuracy. MobileNetV2 showed the worst performance for detecting faces without mask with 72.44% accuracy. Comparison results show that VGG16 can be used effectively to detect faces without mask. This system can be used in crowded public areas to warn people without mask that may help the reduce the risk of pandemic.

The spread of the coronavirus has been the focus the world's attention. The government has issued a new regulation on the coronavirus that obligates the entire community to always wear a face mask and implement social distancing to prevent the spread of viruses. These issues have been impacted by an increase in the use of face masks throughout the world and in Indonesia. Moreover, the World Health Organization (WHO) suggested using medical face masks. Furthermore, in the health care sector, the possible loading of pathogens in sub-micrometer sizes and properties such as splash resistance and the ability to prevent infection by reducing the concentration of inhaled particulates has created limitations on the types of face masks that can be used. Therefore it is necessary to design a face mask sterilizer device that can extend the life span of masks, thereby decreasing the masks consumption. An investigation on mask sterilizer devices based on heat pipes and thermoelectrics was conducted in this study. The objective of this study was to investigate the thermal performance of the mask sterilizer device. The method used was an experimental method using variations in the input voltage on the thermoelectric, namely 9 V, 10 V, and 11 V, and the microcontroller was also adjusted to control the temperature by 70 ℃, 80 ℃, and 90 ℃. The results showed that the thermoelectrics can generate heat with a temperature difference between the hot side and the cold side that can reach 80.11 ℃. In terms of voltage, the greater voltage that was given led to a greater resulting temperature of the sterilization device and reduced the amount of time to achieve the minimum sterilization temperature.KeywordsSterilizationFace maskHeat pipeThermoelectric

With the crisis of the COVID-19 pandemic, it has become apparent in the Philippines that protocols need to be put in place that ensures the health and safety of the people. Included in those protocols is contact tracing and the proper use of masks and shields. The purpose of this research is to develop a system of face mask and face shield detection using image processing with deep learning and thermal scanning for logging system to automate the task of surveying and compliance to wearing of mask and shield. A model for classifying the five classes: face mask, face shield, face mask and face shield, none, and no face was created and trained using the MobileNet architecture, with collected dataset using the Maixduino camera. An overall accuracy of the entire system was found to be at 90%. No face and none classifications have provided results of above 90% in precision, sensitivity, specificity, and F1-Score. While values of the only mask, only shield, and both fluctuate in values in computations, their F1-scores still falls within the range of 80%-90% in performance. The implementation of the MobileNet model on the Maixduino board for was successfully accomplished with considerable classification capability.

Face masks alter the speakers' voice, as their intrinsic properties provide them with acoustic absorption capabilities. Hence, face masks act as filters to the human voice. This work focuses on the automatic detection of face masks from speech signals, emphasising on a previous work claiming that face masks attenuate frequencies above 1 kHz. We compare a paralinguistics-based and a spectrograms-based approach for the task at hand. While the former extracts paralinguistic features from filtered versions of the original speech samples, the latter exploits the spectrogram representations of the speech samples containing specific ranges of frequencies. The machine learning techniques investigated for the paralinguistics-based approach include Support Vector Machines (SVM), and a Multi-Layer Perceptron (MLP). For the spectrograms-based approach, we use a Convolutional Neural Network (CNN). Our experiments are conducted on the Mask Augsburg Speech Corpus (MASC), released for the Interspeech 2020 Computational Paralinguistics Challenge (COMPARE). The best performances on the test set from the paralinguistic analysis are obtained using the high-pass filtered versions of the original speech samples. Nonetheless, the highest Unweighted Average Recall (UAR) on the test set is obtained when exploiting the spectrograms with frequency content below 1 kHz.

In the context of enhancing security authentication, facial recognition technology has become pivotal, replacing conventional authentication methods such as passwords, security tokens and PINs across various sectors. However, the rapid growth of facial recognition technology faced hindrances due to the COVID-19 pandemic, where mandatory face mask usage obscured facial features, challenging existing authentication methods. Regardless, the existence of several methods for face mask detection and recognition highlighted prevalent issues such as poor lighting, varied angles, failed detection for improper use of face masks, computational complexity, difficulty in detecting smaller faces and low-resolution targets have led to suboptimal accuracy rates. Hence, this study aims to address these challenges by introducing a hybrid Convolutional Neural Network (CNN) architecture tailored for Face Mask Detection (FMD) and Masked Facial Recognition (MFR). The models used MobileNetV2 and FaceNet InceptionResNetV1 respectively for FMD and MFR. The proposed models leverage advanced FMD and MFR technologies, contributing to the real-world need for enhanced security in scenarios where traditional methods are insufficient. The models underwent training using five distinct datasets, comprising a total of 10,980 images for FMD across two datasets, and 26,523 images for MFR across three datasets. In the FMD phase, the model achieved exceptional results, attaining a perfect 100% across evaluation metrics such as accuracy, precision, recall, and the f1-score within a training timeframe of an hour. Transitioning to the MFR phase, where the model required approximately one hour and 30 minutes, maintained an outcome of 99.68% across the aforementioned metrics, surpassing the accuracy level of existing models within the meta-analysis. Furthermore, the model underwent testing on a real-time custom dataset designed for MFR evaluation, consisting of 5500 images (i.e., 4400 for training, 550 for validation and 550 for testing) in real-life scenarios. Robustness was assessed under various conditions, resulting in an impressive 99.82% accuracy. The model demonstrated high accuracy in real-time testing. Notably, both the models excel in detecting and recognising masked participants from diverse angles and lighting conditions with minimal computational complexity. Leveraging the pre-trained MobileNetV2 for FMD and FaceNet InceptionResNetV1 with CNN for MFR, the CNN models provide a comprehensive solution. The proposed models surpass existing methods, excelling in accuracy under challenging conditions. This study contributes a versatile and efficient solution, addressing limitations in current approaches and providing robust models for FMD and MFR in diverse sectors.

The rapid growth of facial recognition technology has faced hindrances due to the COVID‐19 pandemic, where mandatory face mask usage obscured facial features, challenging existing authentication methods. Despite the rapid development of several methods for face mask detection and recognition that highlighted prevalent issues such as poor lighting, varied angles, failed detection for improper use of face masks, computational complexity, difficulty in detecting smaller faces and low‐resolution targets, these have led to suboptimal accuracy rates. Hence, this work addresses these challenges by introducing a hybrid convolutional neural network (CNN) architecture tailored for face mask detection (FMD) and masked facial recognition (MFR). The models are developed using MobileNetV2 and FaceNet InceptionResNetV1 with CNN, for FMD and MFR, respectively. Experimental results on both models utilising a total of five distinct datasets, with two for FMD and three for MFR, show the superiority of the developed model in comparison to state‐of‐the art models. In addition, the models are tested in real‐time for both FMD and MFR to determine their robustness, efficiency and accuracy in a real‐time context. For this purpose, a ‘custom real‐time masked face recognition’ (CRMFR) dataset was developed to perform real‐time MFR. Leveraging advanced FMD and MFR technologies, the models contribute to the real‐world need for enhanced security in scenarios where traditional methods are insufficient.

Detecting faces is a prevalent and substantial technology in current ages. It became interesting with the use of diverse masks and facial variations. The proposed method concentrates on detecting the facial regions in the digital images from real world which contains noisy, occluded faces and finally classification of images. Multi-task cascaded convolutional neural network (MTCNN)—a hybrid model with deep learning and machine learning to facial region detection is proposed. MTCNN has been applied on face detection dataset with mask and without mask images to perform real-time face detection and to build a face mask detector with OpenCV, convolutional neural networks, TensorFlow and Keras. The proposed system can be used as an application in the recent COVID-19 pandemic situations for detecting a person wears mask or not in controlling the spread of COVID-19.

Background: The face mask has been used to protect against airborne diseases throughout history. We conducted a historical review of the literature on the origin of the face mask, the scientific evidence of its benefits, and its implications for domestic and international politics. Material and Methods: We performed a comprehensive search for peer- and nonpeer- reviewed literature published between 1905 and 2020. Results: Face mask wearing in hospital settings to prevent disease transmission from health care workers to their patients originates with the first use of the mask in surgery in 1897 by German surgeon Johann von Mikulicz. During the first half of the 20th century, various scientific investigators focused on determining the most effective type of medical mask. The role of the face mask in the general population as a preventive intervention during public health emergencies is supported by historical reports spanning from the European Bubonic Plague in 1619, to the Great Manchurian Plague of 1910-1911, the influenza pandemic of 1918, and the current coronavirus disease in 2019 (COVID-19) pandemic. Although the face mask has helped against airborne disease transmission, its benefits during pandemics have been filtered through the prism of political leanings and geopolitical interests. Conclusions: Our review suggests that while face mask alone cannot stop pandemics, in conjunction with other nonpharmacologic interventions it can be useful in mitigating them. When cooperation rather than division becomes the norm in the global response to pandemics, the face mask can then unite rather than divide us.