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.

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.

Facemask detection using Deep Learning

COVID-19 was raised in the year 2020 which became more dangerous to society. According to the medical results, 100 million confirmed cases and 6 million deaths. This virus became an obstacle to gathering people in public places. This virus has spread all over the world. So, the Government has implemented a facemask policy to prevent the hazardous virus. It is a very difficult task to observe manually in crowded places. Most people are not wearing facemasks properly in public a place which causes the increase of the virus. So, the proposed model will detect the face mask whether the people are wearing it or not. By using, the HAAR-CASCADE technique we can able to detect whether the people are wearing the mask or not. By using this algorithm, we can able to prevent affecting of the virus to the person. This algorithm works effectively for detecting facemasks. The system compares faces with masks and faces without the mask. If people are not wearing a mask, the system detects through the camera and alerts by the alarm sound. The experiment results show the proposed technique achieves a 95% accuracy rate. © 2023 IEEE.

Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset

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.

Drowsiness Detection System for Masked Face Based on Deep Neural Network and Haar Cascade

Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system. In this study we propose drowsiness detection system utilizing current novel classification model such as Deep Neural Network (DNN), combined with Haar Cascade. The DNN is utilized to detect face, while Haar Cascade is utilized for detecting the eyes and its state on the detected face. In this study, due to Covid19 pandemic, we focused on developing the classifiers for detecting the face with mask. Apart from that, our proposed classifiers are also capable of identifying non-masked faces. The experimental result showed that by utilizing DNN and Haar Cascade, our proposed system could reach accuracy, precision, recall, and f1 measure as much as 81%, 88%, 80%, and 84%, respectively. © 2022 IEEE.

IoT Based Intelligent System for Covid - 19 hotspot detection by CNN Crowd Density Algorithm

The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.

Eye Detection from Face Images covered in Face-masks using HAAR features

The contagious illness known as COVID-19 made wearing a mask an essential part of daily life. Mask-covered faces cannot be detected by the current eye detection methods. Many biometric identification systems, like iris recognition, depend on accurate eye detection. Thus, in this study, an efficient method using machine learning for detecting eyes of people wearing mask is presented. Haar-cascade classifier is used to implement real-time eye detection from a live stream via webcam. From the live stream, frames are extracted and saved as images. Dataset was prepared by collecting face images of people wearing mask under various background. Haar-cascade classifier which was trained using 2000 positive and 4000 negative images is used to detect the position of eyes. According to the results on dataset, the system could attain an average accuracy of 96.72%. © 2022 IEEE.

Adopting Haar Cascade Algorithm on Mask Detection System Based on Distance

COVID-19 which has hit almost the whole world, including Indonesia, which has become an epidemic in early 2020. Many cities and districts have enforced to comply with health protocols by using masks. All cities and regencies in South Sumatra are also required to follow health protocols by wearing masks and maintaining distance. So that the Mask Detection System program is a way to overcome public awareness, especially Bina Darma University that the importance of using masks today. In the case of making this mask detection system program using Python and using the Haar Cascade Algorithm. From experiments using the Haar Cascade method, the results show that this system can detect people who use masks and do not use masks. This test is also done by inputting images or videos. Futhermore, in testing this detection system, the approximate distance and angle also need to be considered because it will be very influential. © 2022 IEEE.

Comparative Study of Convolutional Neural Network and Haar Cascade Performance on Mask Detection Systems Using Matlab

Since the outbreak of the COVID-19 virus, various technologies have developed as an alternative to preventing the spread of the COVID-19 virus;one of them is face mask detection. Many methods are used, such as Convolutional Neural Network, Haar cascade classifier, and more. This paper discusses how the system will work with face mask detection and the performance result while running the system against the parameters that can occur during training or direct testing by comparing several different methods. The test results display in the form of a line graph, and the Haar Cascade Classifier method will be displayed in tabular form, with the highest accuracy in the CNN method being 93%, while the Haar Cascade Classifier method is 96% © 2022 Institute of Advanced Engineering and Science (IAES).

Increasing validation accuracy of a face mask detection by new deep learning model-based classification

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask;at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieved lower computational complexity and number of layers, while being more reliable compared with other algorithms applied to recognize face masks. The findings reveal that the model's validation accuracy reaches 97.55% to 98.43% at different learning rates and different values of features vector in the dense layer, which represents a neural network layer that is connected deeply of the CNN proposed model training. Finally, the suggested model enhances recognition performance parameters such as precision, recall, and area under the curve (AUC). © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Face Mask Detection and Recognition System

As today a disease called COVID-19 is causing health crisis and deaths, it became most essential to wear a mask for protecting ourselves from Corona virus. Even in public areas, where is more rush we should wear mask as no virus can spread from person to person if any one of person from public is Corona positive. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID19. So basically in this project we are going to use Python, Keras, OpenCV alongwith MobileNet for this Face Mask Detection System. This includes some steps like data preprocessing, training and testing the model, run and view the accuracy and applying model in the camera. The inputs has provided here are 1000+ images of people with mask and without mask. First the data get processed and then by checking features of each image it will train all the models and the persons with and without mask get separated to two categories: with mask and without mask. If person is wearing mask with 90 or more percent of accuracy, then he will get added to with mask category and person not wearing mask get added to without mask category, so that we can permit with mask person to public areas. © 2022 IEEE.

Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic

Right from the beginning of the COVID-19 outbreak, everyone is aware of the havoc caused by the pandemic. To curb its spread, every healthcare agency and civic body around the globe has been advising to wear masks. However, this necessary practice has posed a significant challenge for the modern-day Facial Recognition technology. Face recognition finds significant application in the security domain that demands speed and accuracy both simultaneously. This requires the system to be highly optimized and efficient. Through this paper, we present a novel approach using Haar cascade classifier for face detection with Local Binary Patterns Histograms (LBPH) face recognizer. This work further goes on to address the various problems that occur when the user wears a mask that covers a different area and percentage coverage of the face resulting in inaccuracies as various tests come with false negatives or false positives. This problem is addressed by making use of a fuzzy-based system that decides the "threshold confidence score" needed to pass the authentication dynamically. Our proposed model for masked Face recognition achieves an accuracy of 86% when a Haar-feature-based cascade classifier with LBPH face recognizer is used standalone which further improves to around 97% when used in conjunction with a fuzzy system. © Scientia Agropecuaria.All right reserved.

Real Time Face Mask Detection by using CNN

The ongoing Covid-19 (coronavirus) outbreak is worsening the worldwide health crisis, impacting our daily lives. Wearing a face mask is the most basic form of prevention against coronavirus and also it is considered as one of the most significant survival suggestions. Nowadays, the correctness of wearing face masks is manually monitored, and it is impossible to alert people in overcrowded areas or public locations. For this reason, machine learning frameworks such as openCV, keras, scikit-learn, and tensorflow are employed. The proposed approach intends to develop a new way to automatically detect the correctness of face mask in human face. If no facemask is detected, the proposed model will inform or alert the concerned person. To detect face, an openCV with haar-cascade classifier is employed. The Convolutional Neural Network (CNN) model is also used to detect or train the proposed dataset, which includes the images of different persons with or without face mask. This technique leverages an accuracy of about 99.1%. © 2022 IEEE.

Covid Prevention System for Face Mask Detection and Temperature Sensing

Currently, the world is experiencing a serious medical crisis as a result of the Corona virus COVID-19, which now has swept the globe. For several countries, combating this disease outbreak has become an unfortunate reality. Wearing a face mask when going outside or meeting with others is essential for prevention. Some irresponsible people, on the other hand, refuse to wear face masks for a variety of reasons. The development of the face mask detector too is critical in this case. To address this problem, a reliable face mask detector must be created. A face mask can be detected using the object detection algorithm. The mask detection algorithm used to detect the face mask was Haar Cascade in OpenCV from Python. According to the results of the experiments, this device can detect whether or not someone is wearing a face mask and can also measure body temperature. Once these validations are completed automatically door gets opened and sanitization is done. © 2022 IEEE.

COVID vision: An integrated face mask detector and social distancing tracker

The effects of the global pandemic are wide spreading. Many sectors like tourism and recreation have been temporarily suspended, but sectors like construction, development and maintenance have not been halted due to their importance to society. Such projects involve people working together in close proximity, thus leaving them susceptible to infection. It is recommended that people maintain social distance and wear a face mask to reduce the spread of COVID-19. To this effect, we propose COVID Vision - a system consisting of convolutional neural networks (CNNs) for a face mask detector, a social distancing tracker and a face recognition model to help people rely less on personnel and maintain the COVID-19 norms and restrictions. COVID Vision is able to detect, with great accuracy, if a person is wearing a mask or just covering their mouth with their hands as well as people's social distancing infractions from a live video in real time. It can also maintain a database of people who have tested positive for COVID-19 or are at risk using facial recognition. © 2022

FACE MASK DETECTION AND IDENTIFICATION OF INDIVIDUALS BY USING CNN ALGORITHM

The COVID19 pandemic has had a significant impact on people's social lives. Due to this pandemic, almost every office, institution, organization in the world suffered a great deal from being practically closed. The World Health Organization (W.H.O) recommended everyone wear a mask whenever they step outside or in a public place. Therefore, it is mandatory to cover your face with a mask at public places, social gatherings, etc. Facemask detection has recently become one of the most important tasks to help society. The advancement of technology has proven that deep learning has shown its effectiveness in recognition and classification through image processing. There are many face detection models created by using several algorithms and techniques. Find whether a person has puton a mask properly or not and identify that person who didn’t puton a mask properly employing their age and gender. The combination of the face mask detection module and age & gender detection module is used. In our paper, the Haar cascade classifier was implemented to detect faces from the input images in the face mask recognition module. We train this module using CNN. We can recognize faces in this model using the Voila Jones technique and Haar-like features. The face detection module and age & gender detection module is trained by using a Convolutional neural network. A model trained by Tal Hassner and Gil Levi is used to implement Age and Gender detection;an alert sound will be a part of the outcome if the person is not wearing a mask properly. For the facemask detection module, the dataset is taken from Kaggle;images of people wearing masks and not wearing masks are gathered from different sources and formed into a dataset to train the model. In this paper, we used the Adience dataset to train age & gender detection and a dataset from Kaggle containing pictures of people’s faces with and without a mask. The model attains an accuracy of 93.42 %for face mask detection and an accuracy of 91.23% for Age and Gender detection. © 2006-2022 Asian Research Publishing Network (ARPN). All rights reserved.

Facial Recognition with Computer Vision

The cutting-edge age of innovation has developed at high speed to make our lives quiet. The progression in security administrations of the advanced world has prompted the working of frameworks and gadgets all the more effectively and precisely by giving the greatest to most extreme wellbeing and security. The significant method of getting our frameworks and gadgets is passwords or passphrases however this has a few bugs which can undoubtedly be broken or hacked, this prompted the appropriation of further developed procedures, i.e., biometric finger impression scanner, this gives sufficient measure of safety to frameworks like opening the telephone and section in schools or workplaces. In any case, examining the current situation of this gigantic pandemic Covid-19, some control is needed for the protection of individuals which advances touchless registration into workplaces, schools, and universities. This examination paper denotes the use of Computer Vision innovation by carrying out python programming language and its libraries. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Virtual Ornament Room Using Haar Cascade Algorithm During Pandemic

Generally, purchaser wishes to attempt all jewels on them and share those photos on social media to get a recommendation. But now, in the present situation, all are avoiding touching activities unnecessarily. It is not so safe due to COVID-19. So without stopping purchaser jewellery attempts, a solution is provided by using an application called virtual ornament room. The proposed scheme is based on creating a Web application that identifies where the human face is located in the frame and superimposes the chosen jewellery on the face using the HAAR Cascade Algorithm. By using the concepts of augmented reality, the digital objects are estimated and placed onto the frame in real time. The system is implemented by using Flask framework and OpenCV, a Python module. This application works with an attached camera, Internet, and a Web browser. From the results, it can be seen all the jewellery can be easily selected by the customers from their home itself during the pandemic situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Face Mask Using Convolutional Neural Network

According to data acquired by the World Health Organization, the worldwide universal of COVID-19 bears harshly hit the realm and bears immediately contaminate eight heaps of human beings in general. Wearing face masks and following cautious public leave behind are two of the embellished protection from harm rules of conduct that need to take the place of honestly held places in consideration of keeping from happening or continuing the spread of the virus. To develop in mind or physically conservative surroundings that contribute to public protection from harm, we suggest an adept data processing machine located in close contact with the genuine in existence-period made or done by a human being to discover two reliable public dissociate themselves and face masks honestly placed by the model ahead of the start of the model to monitor special interests or pursuits and discover rape through photographic equipment. In addition to presenting an alarm to the public, in this proposed structure, we have designed mask detection along which indicates people to wear their mask properly before permitting in to the area which they prefer. We have used machine learning with supports the accuracy for the prediction. © 2022 IEEE.

Measuring Accuracy Towards Facial Video Heart-Rate Estimation Using Haar-Cascade and CNN Method

COVID-19 leads us to have a social distancing even for health-treatment. In this study, we attempt to estimate heart rates in humans using camera-based remote photoplethysmography (rPPG) methods, which are named after conventional PPG methods. The basic concept is focused on capturing minute variations in skin color during the human body's cardiac cycle, which involves the inflow and outflow of blood from the heart to other body parts. We have compared the performance of different methods of Blind Source Separation and face detection which form an integral part in accurately calculating the heart rate. Purpose: The purpose of this method was comparing the actual heart rate with a tuned parameter of Face Video Heart Rate estimation with CNN and OpenCV haar-cascade. Patients and methods: Videos in the dataset are run through a face detection model to get the region of interest for heart rate calculation. Source signals are converted to frequency domain for filtering and peak detection to obtain heart rate estimates Results: Face segmentation using Convolution Neural Network gives better results than the Haar Cascade OpenCV face detection module, which is as expected. Conclusion: Face segmentation using Convolution Neural Network gives better results than the Haar Cascade OpenCV face detection module. CNNs are slower to detect faces than the Open-CV module. Choosing an ROI by segmenting out facial pixels helped to keep the outliers low and therefore increased the robustness. © 2022 IEEE.