Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment

The growing pandemic caused by COVID-19 has been making headlines recently, causing a global disaster with an unavoidable spread. The WHO recommends wearing a face mask and maintaining physical distance. This research can assist in keeping track of social distance and it can also spot face masks. The entire system is set up in public settings. Images and videos are used by this system to identify persons. Frame by frame processing is done on the video. People are recognized by image processing with TensorFlow and OpenCV. MobileNetV2 and ResNet50 are used for detecting the face mask violation. It first recognizes the crowd’s presence before calculating the centroid distances between individuals using an object tracking algorithm and a distance algorithm. In order to assess the social distance between people, the Euclidean distance is typically employed to calculate how far off each pair of centroids of the bounding box is identified from one another. At a distance of six feet, the program can determine the number of pixels in an image. The detection models MobileNetV2 and ResNet50, respectively, obtained accuracy of 97% and 98% with the large dataset, which contains 12000 samples with and without masked faces.

The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24,916 images, carefully categorized into two main folders: “Correct” and “Incorrect” representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical. In the “Correct” folder, images depict individuals correctly wearing their respective face masks, while the “Incorrect” folder contains images of improper face mask usage. To capture variations in face mask application across different demographics, such as age and gender, each subfolder also includes three additional subfolders - Child, Male, and Female. The dataset's diverse content encompasses different face mask types, covering bandana-style, cloth, N95 respirators, and surgical masks, across various age groups and genders. This design ensures a comprehensive representation of real-world scenarios, enabling the evaluation of machine learning algorithms for face mask detection and classification. Researchers can leverage this dataset to develop and assess models that can accurately identify and distinguish between correct and incorrect face mask usage. By contributing to the advancement of face mask detection technologies, this dataset further supports public health initiatives and encourages proper mask-wearing behavior to mitigate the spread of infectious diseases, particularly during times of heightened health concerns such as the COVID-19 pandemic.

Community face masking is possibly of great value in reducing COVID-19 transmission, especially when universally adopted with high compliance. The aim of this study is to investigate the knowledge, common misconceptions, barriers, and the compliance of the community with the use of face masks for the prevention of COVID-19. A validated questionnaire was administered to the participants through a web link by using various social media. The collected data were statistically analyzed for significant differences according to demographic variables. The average knowledge of face masks and their role in preventing COVID-19 transmission was 95.64%, with no differences among most of the demographical factors. Older groups and females demonstrated a better attitude towards wearing face masks than other groups did (p<0.001). Another significant difference in the participant’s attitude was noticed between the various educational levels, employment, and nationality (p<0.001). Of the total respondents, 88.2% encouraged wearing face masks. Misconceptions about wearing face masks were very low. The frequency of wearing face masks at public places, workplaces, or social gatherings was 87.2%, 80.5%, and 47.5% respectively. There was a significant variation in the compliance with wearing face masks between the various groups based on age, gender, nationality, and employment status (p<0.001). The inconvenience in wearing face masks was reported by 36.3%. Face irritation and ear pain were reported by 70.2% and 43.5%, respectively. The inconvenience of wearing face masks with eyeglasses was reported by 44.3% of those wearing eyeglasses. In general, the study demonstrated a good attitude among participants towards wearing face masks. Although the respondents in the study were aware of the benefits of wearing face masks, the barriers may have decreased their desire to do so. These barriers include difficulty in breathing, discomfort, face irritation, and ear pain.

The system is fed with the CCTV footage or a real-time web camera, where the people in the frame get detected and the face mask detection module takes place. It uses the facial recognition and identifies a person without mask, followed by a notification alert message through a mail. Then the social distancing module checks whether the person is in a safe zone by measuring social distancing with nearby people. It alerts as “Please put on the mask. The people near you are not following social distancing.” The CCTV cameras are set-up in places where the system needs to follow the above violation metrics. Extra CCTV cameras can be installed if certain areas are meant to be monitored where CCTV cameras were not previously installed. Then the servers or computers must be installed to run inference of the received footage. The system uses a deep learning model in the computer servers to identify the above specified violation metrics.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

As COVID-19 has been constantly getting mutated and in three or four months a new variant gets introduced to us and it comes with more deadly problems. The things that prevent us from getting Covid are getting vaccinated and wearing a face mask. In this paper, we have implemented a new Face Mask Detection and Person Recognition model named Insight face which is based on SoftMax loss classification algorithm Arc Face loss and named it as Rapid Face Detection and Peron Identification Model based on Deep Neural Networks (RFMPI-DNN) to detect face mask and person identity rapidly as compared to other models available. To compare our new model, we have used previous MobileNet_V2 model and face recognition module for effective comparison on the basis of time. The proposed model implemented in the system has outperformed the model compared in this paper in every aspect.

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995.

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.

An outbreak of the coronavirus (Covid-19) has spread around the world, causing widespread health concerns. The pandemic's reach has spread to nearly every development sector. There have been numerous steps attempted to prevent the in areas where Covid-19 has spread, wearing a face mask is mandatory. The Covid-19 transmission could not have been controlled without it. To top it all off, seeks to detect finegrained wearing states of the face mask: make sure your face is covered with the right mask Without a disguise. There are two very difficult jobs in our job: first, because of an online market for augmented data sets as well as large-scale training datasets. A face mask identification app for mobile devices is presented in this paper. In its entirety, Cloud-based machine learning technology known as Google Cloud ML API automates the process using TensorFlow files to train the model The ML's effectiveness is demonstrated in this research model. The progress of cloud technology is also examined in this paper. Over traditional coding methods, machine learning is becoming increasingly popular.

SCS-Net: An efficient and practical approach towards Face Mask Detection

Due to Covid-19, most of the health care organizations and governments has ordered their citizens to wear face mask to protect themself. This novel research presents a tactical methodology for rapid detection whether a person is wearing a face mask or not. It is entirely different from the existing system; the singular aim of the proposed system is to train the deep learning model with a minimum number of image samples and to operate face mask instance segmentation and along with object box detection. In this work, we proposed a novel and semantic pixel-to-pixel region based deep network which can detect no of face mask instances in different categories pixel wise to organize the segment bounding box and the confidence of the various categories for each pixel. This system experimental output demonstrate that this approach can effectively and precisely detect the face mask with multi-feature combination. It is also reported that our application performance outperforms the existing system.

A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 3 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.

Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks. Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colony-based Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

In 2020, COVID has grown to be a significant public health concern. The virus's contact-transparent properties caused it to propagate quickly. Wearing a mask that stops the transmission is the most effective strategy to prevent this. The first step in lowering the danger is to wear a mask, which cuts the likelihood of contracting the infection by 70%. To resolve this problem, we need the system to keep an eye on the facial mask. This work can be facilitated by a face mask detector based on deep learning (DL) and machine learning (ML) models can make this task easier. Here, we do face mask categorization utilizing several ML and DL techniques. Here, we experimented with various methods such as CNN, Resnet50, Gabor filter, GLCM, SVM, KNN for extracting features and classifying faces with mask and without mask. From the accuracy we got for each model it says CNN performs well on the dataset we took.