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).

A Novel Approach for Identification of Tomato Flu using Deep Learning

The study proposes a novel deep learning-based model for early and accurate detection of the Tomato Flu virus, also known as tomato fever, which has recently emerged in children under the age of five in the Indian state of Kerala. The model utilizes a deep learning method to classify skin pictures and check whether a person is suffering from the virus or not, with an accuracy of 100% and a validation loss of 0.2463. Additionally, an API is developed for easy integration into various web/app frameworks. The authors highlight the importance of careful management of rare viral diseases, especially in the context of the ongoing COVID-19 pandemic. © 2023 Bharati Vidyapeeth, New Delhi.

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.

An Improved Computer Vision based Face Mask Detection and Alerting System

A global healthcare crisis has been declared as a result of the covid-19 nandemic's extensive snread. The coronavirus spreads mostly by the release of droplets from an infected person's irritated nose and throat. The risk of spreading disease is highest in public gathering places. Wearing a facial mask in public is one of the greatest ways, according to the World Health Organization, to avoid getting an infectious disease. This research work proposes an approach to human face mask detection using TensorFlow and OpenCV. Whether or not a character is wearing a mask is indicated by an enclosing field drawn around their head. An alert email will be sent to a person whose face is in the database if they make a call without a mask worn. © 2023 IEEE.

Application of AI techniques for Face Mask Detection

Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we'll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine. © 2022 IEEE.

An Effective Technique using MobileNet-V2 for Face Mask Detection

Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc. © 2023 IEEE.

Deep Learning based Face Mask Detection system with Automatic Indigation

The COVID-19 pandemic has quickly had an impact on our day-to-day lives, as well as on the movement of goods and people around the world. It has recently been common practice to shield one's face by using a mask. In the not too distant future, many businesses that provide public services will need their clients to correctly wear masks in order for them to receive those services. As a result, the detection of face masks has evolved into an important mission in the service of worldwide society. In this post, a relatively straightforward approach to achieving this goal is presented using basic machine learning tools like TensorFlow, Keras, OpenCV, and Scikit-Learn. The suggested method accurately locates the face inside the image before determining whether or not it is covered by a mask. While doing a surveillance task, it is capable of detecting a mask as well as a moving face. To properly detect the presence of masks without over-fitting, we look into numerous options for optimizing the values of the parameters in the Sequential Convolutional Neural Network model. © 2022 IEEE.

Identifying Face Masks with OpenCv and Deep Learning

During the course of this epidemic, the Corona virus had a significant influence not only regular lives but also on international business. Protecting one's appearance has recently emerged as a widespread fashion trend and can now be considered the norm. In the present day or in the future, a large number of individuals will be obliged to wear masks in order to protect not only themselves but also the people around as well as the surrounding area. Face recognition has emerged as an increasingly vital tool in the fight against global terrorism. As part of this work, we are developing an AI system that will be able to determine whether or not a person is concealing their identity by wearing a mask. It will be of assistance to us in preventing the virus from spreading across the environment. In order to construct this work, we require the assistance of Machine Learning (ML), deep learning (DL), and Neural Network (NN), all of which will assist us in realizing the purpose of this work. We needed jupyter notebook in order to complete this work, and we also needed to install numpy, opencv, tensorflow, and numpy as well as a learning tool. This strategy will assist us in identifying the individual who is concealing their identity by wearing a mask in the imageand in real life picture. Additionally, it is able to recognize and distinguish a moving mask or face. © 2022 IEEE.

Face Mask Detection Using Convolution Neural Network

Face recognition in the industry now is playing an important role in each sector. Each person has different type of features and face;therefore, each identity is unidentical. In this COVID outbreak, a major crisis has occurred due to which preventions are to be made. One such prevention is use of a face mask which is very much important. Nowadays, various firms and organizations are using facial recognition systems for their own general purpose. We all know that it has now been a crucial task to wear a mask every time, when we go somewhere. But as we know it is not possible to keep track of who wears a mask and who does not. We make the use of AI in our daily life. We achieve this with the help of a neural network system, which we train so that it can further describe people's features. Even though the original dataset was limited, the Convolutional Neural Network (CNN) model achieved exceptional accuracy utilizing the deep learning technique. With the use of a face mask detection dataset that contains both with and without face mask photographs, we are able to recognize faces in real-time from a live webcam stream using OpenCV. We will develop a COVID-19 face mask detection system using our dataset, along with Python, OpenCV, Tensor Flow, and Keras. © 2022 IEEE.

A Model to Detect Face Mask Using Deep Learning

Coronavirus pandemic started spreading in 2019 and is still spreading until now in 2021 all over the world. Due to this the healthcare sectors are going on crisis all over the world. One basic protective measure that we can implement in our daily life is wearing a face mask. Wearing a mask properly can control the spread of this virus to a great extent. Various regions have made wearing face mask mandatory to prevent spread of this virus. In this paper we have proposed a deep learning-based model to detect face mask using python, OpenCV, TensorFlow and it can be used in our health care sectors. © 2022 IEEE.

Advances and critical assessment of machine learning techniques for prediction of docking scores

Here we present three distinct machine learning (ML) approaches (TensorFlow, XGBoost, and SchNetPack) for docking score prediction. AutoDock Vina is used to evaluate the inhibitory potential of ZINC15 in-vivo and in-vitro-only sets towards the SARS-CoV-2 main protease. The in-vivo set (59 884 compounds) is used for ML training (max. 80%), validation (5%), and testing (15%). The in-vitro-only set (174 014 compounds) is used for the evaluation of prediction capability of the trained ML models. Contributions to the prediction error are analyzed with respect to compounds' charge, number of atoms, and expected inhibitory potential (docking score). Methods for the prediction error estimation of new compounds are considered, yet critically rejected. The ML input weighted with respect to the desired property (i.e., low docking score) in the machine learning models shows to be a promising option to improve the ML performance. Proposed models provide significant reduction in number of intriguing compounds that need to be investigated. © 2023 Wiley Periodicals LLC.

Real-time Face mask Detection Using Deep Learning and MobileNet V2

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Innovative and efficient CNN structure works for chest X-ray images classification

In the era of global transmission of COVID-19, it is a challenge for physicians to efficiently and accurately use chest Xray images to diagnose whether a patient is positive or not. The application of deep learning and computer vision in medical image processing solves this problem, but a highly accurate method is still needed. In this research, we proposed an innovative CNN structure used for chest X-ray classification. Based on deep learning and CNN, this new architecture has an efficient training process and the performance of accuracy is better than other classic nets. The best accuracy on the test dataset is 97.68%. It has competitive advantages over AlexNet, LeNet-5, and Vgg-16. Dropout, Data augmentation, and Grad-CAM technique are added to improve performance. © 2022 IEEE.

Pneumonia image classification based on convolutional neural network

This letter introduces an improved convolutional neural network (CNN), which is used to classify and recognize different types of pneumonia using chest CT images. This classifying model is built and trained on thousands of real clinical chest CT images, which respectively belong to patients with viral pneumonia, patients with bacterial pneumonia, patients with COVID-19, and nonpatients. To richen the dataset and avoid over-fitting, pre-processing methods are recommended. Then the paper elaborates the structure of the new network and compares the performance of different optimizers in this dataset. Finally, the accuracy, specificity, precision, sensitivity, and F1-score of the model are calculated to quantitatively evaluate the performance of this model. The final training accuracy is about 97.9%, and the test accuracy is 91.8%. © 2022 IEEE.

Pneumonia image classification based on convolutional neural network

This letter introduces an improved convolutional neural network (CNN), which is used to classify and recognize different types of pneumonia using chest CT images. This classifying model is built and trained on thousands of real clinical chest CT images, which respectively belong to patients with viral pneumonia, patients with bacterial pneumonia, patients with COVID-19, and nonpatients. To richen the dataset and avoid over-fitting, pre-processing methods are recommended. Then the paper elaborates the structure of the new network and compares the performance of different optimizers in this dataset. Finally, the accuracy, specificity, precision, sensitivity, and F1-score of the model are calculated to quantitatively evaluate the performance of this model. The final training accuracy is about 97.9%, and the test accuracy is 91.8%. © 2022 IEEE.

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Convolution Neural Network Based Covid -19 Face Mask Detection and Recognition

The coronavirus is devastating global health. Ac- cording to WHO guidelines, wearing a mask and keeping a 6-foot distance between people can help to prevent the spread of COVID 19. As a condition of the international COVID-19 outbreak, protective equipment, the most vital of which is a face mask, is required. Wearing a face mask in public is a good way to be safe. This project seeks to develop a real-time, GUI- based face detection and identification system using machine learning. Tensor Flow, Keras, Scikit-learn, and Open CV are used to develop a Convolutional Neural Network (CNN) model to make the technique as accurate as possible. Principal Component Analysis (PCA) and the HAAR Cascade Algorithm are two components of the proposed methodology. If the person in front of the camera is wearing a mask, the classification algorithm's result will be displayed by a green rectangle overlaid around the region of the face;otherwise, it will be represented by a red rectangle superimposed around the area of the face. © 2022 IEEE.

Face Mask Detection using an Automated Hybrid Deep Learning Method in the COVID Scenario

Currently, in light of the health catastrophe due to the COVID-19 which has been unfolding all over the world. Wearing a defensive mask has ended up a substitute normal. Face recognition technology is most commonly implemented for surveillance and other applications. Traditional machine learning classifiers as well as deep transfer learning classifiers have been used to accomplish the face mask detection mechanism. In this paper, two hybrid deep learning models MobileNetV2-SVM and MobilNetV2-KNN has been proposed for the task of face mask detection. The models involve two processes: feature extraction and classification. For initialization, the MobileNetV2 pre-trained weights from ImageNet were employed, and during training, data augmentation and resampling were applied. By integrating the model with an SVM classifier and a KNN classifier, the model is further refined, creating hybrid models that are effective in terms of processing. The Kaggle dataset of 45000 images (22582 images are masked and 23423 images that are unmasked) of the proposed model/system is trained using MobilenetV2 and classified using SVM and K-NN algorithm in different models. Various machine learning frameworks were used like pandas, TensorFlow, Keras and NumPy. The accuracy achieved by the SVM model is 98.17% and 95.22% accuracy are achieved by using the K-NN classifier. © 2022 IEEE.

Intelligent Real-Time Face Mask and Temperature Recognition Using Deep Learning on Raspberry Pi

There has been a steady and significant growth of the advancement in computer vision systems for face masks and temperature tracking. The World Health Organization introduce strict measures to prevent the spread of the coronavirus disease. This paper attempts to create a highly accurate and real-time approach that can effectively detect non-mask trying to enforce to wear mask in order to contribute to community health. For the purpose of detecting face masks, a hybrid model combining deep and regular machine learning will be utilized. We will use OpenCV to recognize faces in real time from a live feed via the Camera module using a dataset that includes images with and without masks and send the data to the cloud for visualization and further analysis. As a main part of the solution, we proposed embedded system with tools utilizing Python, OpenCV, and Tensor Flow with using computer vision and deep learning. To make it cost efficient, quick, scalable, and effective the whole process for detection of face mask is carried out on Raspberry Pi. This project enables improved control over the information already provided and strongly points out the deployment of our method to stop the local transmission from spreading and decrease the possibility of human coronavirus disease carriers. © 2022 IEEE.

Intelligent Face Mask and Body Temperature Detection System using Machine Learning Algorithm

Face mask and body temperature detection is necessary for current pandemic period. Detecting face mask and body temperature helps in decreasing or to avoid spreading of COVID cases especially in crowded areas. The main purpose of face mask recognition and temperature prediction system is to find whether a person is wearing a mask or not and to check the body temperature. With the help of deep neural network based Convolution Neural Network algorithm, face mask has been recognized. For body temperature, LM35 temperature sensor is used. This system undergoes data pre-processing, training, detecting face mask and temperature. By using MobileNet V2, Frontcascadexml file, tensor flow and keras software library the face mask is detected. Then, the result is send to the Arduino microcontroller and displays that the face mask is detected or not by using LED. If the mask is not weared by the person, buzzer will be alarmed. Similar procedure was carried out for monitoring the temperature of a person using LM35 temperature sensor. The main advantages of MobileNet V2 are higher performance, lesser network size and minimum number of parameter are required. © 2022 IEEE.