An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person's face into a face print, which is then stored in a database to verify an individual's identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications. © 2023 IEEE.

Automated Contactless Continuous Temperature Monitoring System for Pandemic Disease Controlling Infrastructures

People are being thermally screened in hospitals and in such facilities, all the data collected must be stored and displayed. The person responsible for keeping track of people's body temperatures must put in more time and effort. This approach is a tedious task, especially during times of dealing with the pandemic diseases like Covid-19. Hence, in this paper, an automated contactless continuous temperature monitoring system is designed to eliminate this time-consuming process. If a person's temperature is too high, that is, higher than the usual temperature range, the system records it and monitors it continuously via a mobile application. In this paper, we present the development of an Automated contactless continuous body temperature monitoring system using a Raspberry Pi camera and mobile application. © 2023 IEEE.

Baseline Estimation in Face Detection for AI Proctored Examinations through Convoluted Neural Networks

The COVID-19 pandemic took the world by surprise, and everything came to a halt. The education sector had to adjust accordingly by shifting to online learning. If the online delivery experience was overall successful, assessment integrity becomes questionable as examinees still manage to circumvent the anti-plagiarism mechanism put in place. In this paper, we propose an artificial intelligence solution using face and head pose detection to estimate the neutral position of the examinee which will form the basis to detect any suspicious behavior. The resulting implementation achieved a 97% accuracy when detecting the examinee in the frame and a 98% accuracy when there are multiple faces detected. © 2023 IEEE.

A Novel Real-time Automated Face Classification and Detection system using Machine Learning Technique

In recent days, Image classification and detection technique has become an important and more essential in the Image processing research field. Creating effective face detection is an essential aspect of handling the detection mechanism, Tracking mechanism and Validation mechanism. The classical methods used for face detection do not have sufficient output. This research paper presents various studies and how machine learning methods are become to solve many challenges present in the face detection system. The first phase of work has a classification model with support vector machines, decision trees and Hybrid Ensemble Transfer learning algorithm. The second phase of work is investigated with real-the world's most popular dataset from World Masked Face Image Dataset and Label Faces in the wild (RMFD). Moreover, the experiment, results show how better accuracy and fast computation which has been achieved by Hybrid Ensemble algorithm with SVM and Decision Trees machine learning techniques. This research helps to assist many social applications such as during pandemics like covid-19 and personal identity, it can be verifying the mask-worn persons. © 2023 IEEE.

COVID-19 Face Mask Classification Using Deep Learning

The COVID-19 pandemic has triggered a global health disaster because its virus is spread mainly through minute respiratory droplets from coughing, sneezing, or prolonged close contact between individuals. Consequently, World Health Organization (WHO) urged wearing face masks in public places such as schools, train stations, hospitals, etc., as a precaution against COVID-19. However, it takes work to monitor people in these places manually. Therefore, an automated facial mask detection system is essential for such enforcement. Nevertheless, face detection systems confront issues, such as the use of accessories that obscure the face region, for example, face masks. Even existing detection systems that depend on facial features struggle to obtain good accuracy. Recent advancements in object detection, based on deep learning (DL) models, have shown good performance in identifying objects in images. This work proposed a DL-based approach to develop a face mask detector model to categorize masked and unmasked faces in images and real-time streaming video. The model is trained and evaluated on two different datasets, which are synthetic and real masked face datasets. Experiments on these two datasets showed that the performance accuracy rate of this model is 99% and 89%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detection Using Multi-Task Cascaded Convolutional Neural Networks

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. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

REAL-TIME FACE DETECTORS AND CLASSIFICATION PERFORMANCE IMPLEMENTING RESNET-50 MODEL FOR FACE MASK-WEARING CONDITIONS

The World Health Organization requires the community to wear a face mask to avoid transmission of COVID-19. The study investigates the performance of face detectors and evaluates the classification performance based on face mask-wearing conditions. The study built a total of 13,806 datasets that recorded an overall classification performance of 98%. The findings show that Multi-task Cascade Convolutional Neural Networks outperformed the other face detectors with an average score of 70% in accordance to distance, angles, occlusions, and multiple detections across given set conditions. Furthermore, the model recorded an accuracy performance of 83% for "correct wearing of face mask", 91% for "incorrect wearing of face mask", and 95% for "no face mask". However, despite the promising performance rates, the identified best face detector decreases when the given conditions are set to a higher level. To further improve and optimize the face mask-wearing conditions, the study highly recommends employing both statistical and mathematical analysis. © School of Engineering, Taylor's University.

Fatigue detection for public transport drivers under the normalization of epidemic prevention

Several studies have shown that fatigue driving is one of the important causes of public transport safety accidents. With the outbreak of the COVID-19, the wearing of masks by public transport drivers presents new challenges for computer-based visual fatigue detection. In order to achieve the goal of accurately capturing the landmark information of the face even when the face is occluded by a large area, we adopt the DNN-based face detection method which has the highest accuracy and the best occlusion resistance. When the driver's face is blocked, the landmark information of the blocked face can be accurately detected by using our optimized face landmark detector. The accuracy rate of landmark recognition can reach 97.80%. On this basis, we calculate the driver's eye information, mouth information and the driver's head deflection angle information in real time as the judgment indicators of the degree of fatigue to comprehensively evaluate the driver's fatigue state. And use mathematical methods to fuse indicators in real time, classify the driver's fatigue state according to the value of the fusion indicators, and adopt different early warning methods for different levels of fatigue. In addition, in order to further improve the accuracy of the detection results and exclude the influence of other facial behaviors on our fatigue judgment indicators, we propose a kinetic energy calculation formula for facial organs based on the improved optical flow method. According to the different kinetic energy of facial organs in different states, which can accurately distinguish the different behaviors of the same facial organs such as blinking and closing eyes, yawning and speaking, which significantly increases the robustness and generalization ability of the detection program. The final experimental results show that the correct rate of the method for determining the degree of fatigue of the driver and passengers can reach 98.40% and 92.30% respectively when the driver does not wear a mask or wears a mask. © 2022 IEEE.

A Face Mask Detection Algorithm Based on YOLOv5

As a new machine learning method, deep learning has been widely used in computer vision. YOLOv5, a target detection algorithm based on deep learning, has a good detection effect. In the case of COVID-19, masks should be worn correctly in public places. Therefore, it is urgent to design an accurate and effective face mask detection algorithm. To solve the problem of mask-wearing detection, a face mask detection algorithm based on YOLOv5 is proposed. The main research contents include training of the YOLOv5 model, verification of face mask detection function, and analysis and comparison of detection effects of three different sizes of detection models: YOLOv5s, YOLOv5m and YOLOv5l. The proposed model realizes the mask detection function and obtains the advantages and disadvantages of different scale models through performance evaluation. The maximum mAP of the model reached 88.1%, with good detection accuracy. © 2022 IEEE.

Masked Face Recognition Dataset and Application

During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.. IEEE

Face detection and mask detection based on convolutional neural network

In the covid-19 pandemic is not completely over today, wearing masks travel is still a necessary means to reduce the risk of transmission of the virus. To solve the problem of low efficiency and low accuracy in face mask detection, a convolutional neural network detection model is proposed in this paper. The paper first gives a brief introduction to the basic principles of convolutional neural network, and then builds on the MTCNN network structure, combining the popular Python language, Keras and tensorflow-gpu, this paper constructs a reasonable and effective neural network model for face detection, and then completes the detection of whether a face wears a mask, finally, the simulation results show that the proposed model has good performance and robustness in the presence of occlusion, different angles, dim light and different expressions. © 2022 IEEE.

An Improved Machine Learning Approach to Detect Real Time Face Mask

Several nations have implemented health protocols like maintaining a particular measure of distance from each other and use of face masks when going out in public, in an effort to stop or at least reduce the spread of Covid-19. However, manually checking whether each person have put on a mask or not is a tiring job, and is possible only if there is a particular person assigned specially for that. This paves way for the need of an electronic device or a machine that would identify whether a person has worn mask or not. Thus, this research proposes a face mask detection system using a machine learning algorithm known as Support Vector Machine (SVM). After creating and preprocessing the dataset, training the model, and evaluating the final model, an accuracy of 98% has been obtained. The model can further be developed and used in real time scenarios to detect faces without a mask and pass those faces separately into a neural network with the help of CNN to easily find out his/her identity, and punish accordingly. © 2023 IEEE.

Facial Recognition-Based Attendance and Smart COVID-19 Norms Monitor

Attendance is an important part of the academic environment. The manual method of marking student attendance is time-consuming and also not accurate. So, the use of biometric attendance is a better alternative to the manual method. There are many biometric techniques that can be considered to design an automated system to mark attendance. Facial recognition is one such biometric technique that can be used. In this paper, we propose the implementation of facial recognition where the attendance is marked by recognizing the faces detected in the video feed from the classroom. We are in the midst of the once in a century crisis, ever since the COVID-19 pandemic broke out it has become imperative to accommodate certain behavioral changes in our day to day lives, one such major change which is essential to curb the spread of COVID-19 is to wear a face mask, and thus, the facial recognition-based attendance adds another advantage by recognizing the faces even though students would be wearing the masks. Another important measure that needs to be followed to contain the spread of COVID-19 is to ensure social distancing in all public spaces;hence, there is a need to ensure that social distancing norms are followed by the students. So, we propose implementation of a system to monitor the social distancing among the students. Further, we propose to implement a COVID-19 vaccination status monitoring system using which we can monitor the vaccination status of the individuals through the video feed from the classroom. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Real-Time Face Mask Detection from CCTV Video Frames using Deep Neural Networks

The coronavirus, commonly known as SARS COVID-19, is causing a pandemic that is affecting individuals all over the world. The spread of the virus compelled the authorities to impose a rigorous lockdown on its citizens. Every person in society may experience a variety of issues as a result of this. According to WHO (World Health Organization) regulations, the sole method to halt the virus's spread is to wear a face mask. Therefore, the suggested approach makes sure that everyone appropriately wears a face mask in public locations. The objective of this approach is to detect people without face masks and people who wear facemasks incorrectly in social environments. This system consists of multiple face detection modules to find the area of interest within the video frames. In the next level, using the trained Deep Learning model, the presence of a mask is detected and faces without mask and faces wearing masks incorrectly are highlighted. The dataset for face mask identification comprises of 8190 photos with unique facial annotations from the Kaggle and RMFD datasets that come into two categories: "with mask” and "without mask”. © 2022 IEEE

Turnstile Access based on Facial Recognition and Vaccine Passport Verification

This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN. © 2022 IEEE.

Joint Masked Face Recognition and Temperature Measurement System Using Convolutional Neural Networks.

With the outbreak of COVID-19, epidemic prevention has become a way to prevent the spread of epidemics. Many public places, such as hospitals, schools, and office places, require disinfection and temperature measurement. To implement epidemic prevention systems and reduce the risk of infection, it is a recent trend to measure body temperature through non-contact sensing systems with thermal imaging cameras. Compared to fingerprints and irises, face recognition is accurate and does not require close contact, which significantly reduces the risk of infection. However, masks block most facial features, resulting in the low accuracy of face recognition systems. This work combines masked face recognition with a thermal imaging camera for use as an automated attendance system. It can record body temperature and recognize the person at the same time. Through the designed UI system, we can search the attendance information of each person. We not only provide the design method based on convolutional neural networks (CNNs), but also provide the complete embedded system as a real demonstration and achieve a 94.1% accuracy rate of masked face recognition in the real world. With the face recognition system combined with a thermal imaging camera, the purpose of screening body temperature when checking in at work can be achieved.

RRFMDS: Rapid Real-Time Face Mask Detection System for Effective COVID-19 Monitoring.

The primary mode of COVID-19 transmission is through respiratory droplets that are produced when an infected person talks, coughs, or sneezes. To avoid the fast spread of the virus, the WHO has instructed people to use face masks in crowded and public areas. This paper proposes the rapid real-time face mask detection system or RRFMDS, an automated computer-aided system to detect a violation of a face mask in real-time video. In the proposed system, single-shot multi-box detector is utilized for face detection, while fine-tuned MobileNetV2 is used for face mask classification. The system is lightweight (low resource requirement) and can be merged with pre-installed CCTV cameras to detect face mask violation. The system is trained on a custom dataset which consists of 14,535 images, of which 5000 belong to incorrect masks, 4789 to with masks, and 4746 to without masks. The primary purpose of creating such a dataset was to develop a face mask detection system that can detect almost all types of face masks with different orientations. The system can detect all three classes (incorrect masks, with mask and without mask faces) with an average accuracy of 99.15% and 97.81%, respectively, on training and testing data. The system, on average, takes 0.14201142 s to process a single frame, including detecting the faces from the video, processing a frame and classification.

UpAIsthiti: A Touchless Attendance System

Managing attendance is a vital task for every institution. Considering the COVID pandemic where many organizations have resorted to online mode of working, it has become imperative to maintain social distancing and digitize various processes. Thus, for maintaining attendance of the students of schools/colleges or employees of a company, a touchless attendance system is required that records the attendance by capturing faces and does not waste time. This one-of-a-kind application uses a client–server model and captures the faces of students/employees through video feeds from mobile phone cameras, and the images are sent to a server, where image processing is used to process the faces. Further, with the help of dlib and the face recognition library, it identifies the faces and records the attendance in the software itself. The processed image is again sent back to the client android application, and the user gets notified about their attendance. Additional functionalities for data analysis and updating data have also been added to the system. Thus, the whole attendance system is an effort to make the attendance activity easy and efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Advanced Face Mask Detection System

COVID-19 pandemic caused a lot of loss and it is really important to give attention to technologies and ways to slow its spread and eventually stop it. Now in past few months, the government tried to open various sectors of society such as school gyms, etc. but failed and had to shut them down again due to increase in cases;we have seen a gradual increase in cases when the government tries to open these sectors because many persons were not so much attentive and were not following protocols properly. One of them is not wearing proper masks. Mask is a non-pharmaceutical measure that is used against primary spread of COVID by droplets. For people to follow protocols and wear masks, we are proposing a face mask detection device which will be effective to make people more aware to wear face masks by warning them who are not putting masks on their faces and restricting them to enter public places such as school, colleges, gym, etc. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

MASKED FACE RECOGNITION VIA SELF-ATTENTION BASED LOCAL CONSISTENCY REGULARIZATION

With the COVID-19 pandemic, one critical measure against infection is wearing masks. This measure poses a huge challenge to the existing face recognition systems by introducing heavy occlusions. In this paper, we propose an effective masked face recognition system. To alleviate the challenge of mask occlusion, we first exploit RetinaFace to achieve robust masked face detection and alignment. Secondly, we propose a deep CNN network for masked face recognition trained by minimizing ArcFace loss together with a local consistency regularization (LCR) loss. This facilitates the network to simultaneously learn globally discriminative face representations of different identities together with locally consistent representations between the non-occluded faces and their counterparts wearing synthesized facial masks. The experiments on the masked LFW dataset demonstrate that the proposed system can produce superior masked face recognition performance over multiple state-of-the-art methods. The proposed method is implemented in a portable Jetson Nano device which can achieve real-time masked face recognition. © 2022 IEEE.