Automated Contactless Attendance System

Attendance marking in a classroom is a tedious and time-consuming task. Due to a large number of students present, there is always a possibility of proxy. In recent times, the task of automatic attendance marking has been extensively addressed via the use of fingerprint-based biometric systems, radio frequency identification tags, etc. However, these RFID systems lack the factor of dependability and due to COVID-19 use of fingerprint-based systems is not advisable. Instead of using these conventional methods, this paper presents an automated contactless attendance system that employs facial recognition to record student attendance and a gesture sensor to activate the camera when needed, thereby consuming minimal power. The resultant data is subsequently stored in Google Spreadsheets, and the reports can be viewed on the webpage. Thus, this work intends to make the attendance marking process contactless, efficient and simple. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Comprehensive Review on Access Control Systems amid Global Pandemic

In the contemporary time of technology, security is the utmost concern for every building automation system. Access Control Systems are the backbone of any security system being employed in any intelligent building, and can be operated in a biometric or non-biometric manner. There are various types of recognition systems available, depending upon the required level of safety and security. The ongoing pandemic has challenged and tested Access Control System in many aspects.This paper aims to review the various forms of access control systems and their viability in the context of COVID-19. It is found that some access control solutions fail to provide the required security during this global epidemic due to their contact-based operations. So, in the midst of the worldwide pandemic, a realistic integrated electronic access control system can be designed to meet the requirements of users. © 2022 IEEE.

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.

Localization using Multi-Focal Spatial Attention for Masked Face Recognition

Since the beginning of world-wide COVID-19 pandemic, facial masks have been recommended to limit the spread of the disease. However, these masks hide certain facial attributes. Hence, it has become difficult for existing face recognition systems to perform identity verification on masked faces. In this context, it is necessary to develop masked Face Recognition (MFR) for contactless biometric recognition systems. Thus, in this paper, we propose Complementary Attention Learning and Multi-Focal Spatial Attention that precisely removes masked region by training complementary spatial attention to focus on two distinct regions: masked regions and backgrounds. In our method, standard spatial attention and networks focus on unmasked regions, and extract mask-invariant features while minimizing the loss of the conventional Face Recognition (FR) performance. For conventional FR, we evaluate the performance on the IJB-C, Age-DB, CALFW, and CPLFW datasets. We evaluate the MFR performance on the ICCV2021-MFR/Insightface track, and demonstrate the improved performance on the both MFR and FR datasets. Additionally, we empirically verify that spatial attention of proposed method is more precisely activated in unmasked regions. © 2023 IEEE.

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.

Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so early and simple detection of tuberculosis has been more and more important. In our study,we focused on tuberculosis diagnosis by using the chestX-ray image, the essential input for the radiologist's profession, and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images. We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types. We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models. Our experiments were carried out by applying three different architectures, Alexnet, Resnet, and Densenet, on international, Vietnamese, and combined X-ray image datasets. After training, all models were verified on a pure Vietnamese X-rays set. The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC (Area under the Receiver Operating Characteristic Curve), sensitivity, specificity, and accuracy. In the best strategy, most of the scores were more than 0.93, and all AUCs were more than 0.98. © 2023 Tech Science Press. All rights reserved.

Challenges for Electronic Access Control Systems amid pandemic: A Survey

To meet the demands for highest level security of today's world, a sophisticated security management system is essential. An access control system generally categorized into biometric and non-biometric types based upon contact or contactless in operation. This research work aims to survey the preferences of people, for understanding the role and need of access control systems during the difficult pandemic situation through an online survey. This survey finds that various access control solutions fail to provide the required security during this worldwide pandemic due to their contact-based operations. Henceforth, a feasible integrated electronic access control system requires to be adopted to fulfill the expectations of users amid global pandemic. © 2022 IEEE.

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.

Study on the Effect of Face Masks on Forensic Speaker Recognition

The COVID-19 pandemic has led to a dramatic increase in the use of face masks. Face masks can affect both the acoustic properties of the signal and the speech patterns and have undesirable effects on automatic speech recognition systems as well as on forensic speaker recognition and identification systems. This is because the masks introduce both intrinsic and extrinsic variability into the audio signals. Moreover, their filtering effect varies depending on the type of mask used. In this paper we explore the impact of the use of different masks on the performance of an automatic speaker recognition system based on Mel Frequency Cepstral Coefficients to characterise the voices and on Support Vector Machines to perform the classification task. The results show that masks slightly affect the classification results. The effects vary depending on the type of mask used, but not as expected, as the results with FPP2 masks are better than those with surgical masks. An increase in speech intensity has been found with the FPP2 mask, which is related to the increased vocal effort made to counteract the effects of hearing loss. © 2022, Springer Nature Switzerland AG.

A Framework for Masked-Image Recognition System in COVID-19 Era

Face Recognition techniques have been widely developed and used for many years. Several approaches and models are adopted and successfully used to perform face recognition in airports, supermarkets, banks, etc. However, with the emergence of the COVID-19 pandemic, the whole world came across the requirement to use face masks. The mask’s partial covering of the face makes some well-known face recognition algorithms perform poorly or even fail. This paper has developed a real-time framework to detect, recognize, and identify people to authenticate them before accessing an app, device, or location. The newly created framework offers a unique set of capabilities, including the ability for users to select from various authentication methods based on their preferences or circumstances. The application’s face recognition section uses cutting-edge AI and computer vision algorithms to offer the user accurate face detection and recognition, even when the face is partially hidden behind a mask. © 2022, Springer Nature Switzerland AG.

A Wearable Warning System Design for Mask Recognition via AR Smart Glasses

Under the influence of COVID-19, various studies have shown that the most important transmission of the epidemic is droplet infection, it is the most effective way to control the epidemic by wearing a mask in a safe range. To confirm the situation of masks-wearing in public, a useful way is to use image-recognition technology to detect the people in the field. On the other hand, with the continued development of wearable devices, smart glasses have been widely used in many files such as handicapped person support. Based on the previous researches, it is already possible to incorporate facial recognition technology into smart glasses. Especially, the application of Augmented Reality (AR) technology on smart glasses can provide users with a lot of additional information, for example, to highlight the targets who been identified. Therefore, to identify the people who are not wearing masks more effectively, in this paper we try to design and wearable mask recognition warning system by using the AR smart glasses. The system can supply the warning messages about the person without masks in both visual and auditory way to the user to support the users including the handicapped persons who not being able to hear or see. The results of this study may provide guidelines to develop the epidemic prevention system and offers useful insights for the supporting of handicapped persons. © 2021 IEEE.

Robust Masked Face Recognition via Balanced Feature Matching

Wearing a facial mask has become a must in our daily life due to the global COVID-19 pandemic. However, the performance of conventional face recognition systems severely degrades for faces occluded by masks. How to combat the effect of occlusion on face recognition is an important issue. However, the performance of existing methods developed for masked face recognition unpleasantly degrades when dealing with unmasked faces. To address this issue for real-world applications, where the gallery image or the probe image may be a masked or unmasked face, we propose the concept of balanced facial feature matching and, based on it, design a robust masked face recognition system. The matching is balanced because it is performed on features extracted from corresponding facial regions. The system consists of a classification network and two feature extractors. The classification network classifies an input face image into a masked face or an unmasked face. One feature extractor extracts the feature of a full face, and the other uses a guided perceptual loss to focus the feature extraction on the non-occluded part of the face. The system is tested on both synthetic and real data. The face verification accuracy is improved by 2.4% for the synthetically masked LFW dataset, 1.9% for the MFR2 dataset, and 5.4% for the RMFD dataset. The results further show that the system improves masked face recognition while preserving the performance of unmasked face recognition. © 2022 IEEE.

Efficient Face Recognition System for Education Sectors in COVID-19 Pandemic

COVID-19 has completely startled the education sectors. Educational organisations are seeking technological innovations to execute their academic activities smoothly e.g. online classes, exams, meetings etc. Face recognition system is one of the main technological innovations that is helping the education sectors to verify the student's identification. In this paper, the authors aimed to explore and develop an efficient face recognition system. These systems require enormous visual information. Visual information holds a large set of features that needs large spatial occupancy. Convolutional neural networks (CNNs) are the most suitable techniques to classify large spatial datasets. We proposed an experimentally based 8 layered tuned CNN model comprising four convolution layers, three max-pooling layers, and one dense layer. The proposed model is trained on 14 face classes and analysed the recognition performance based on different image resolutions, activation functions, batch sizes and optimisers. The proposed model performance has been compared to the state-of-the-art methods and achieved high face recognition. © 2021 IEEE.

Effects of Preprocessing in Person Identification Using Ear Features

Currently, face masking is strongly recommended for people when being outside in order to prevent the COVID-19 spread. However, by doing so, the face area is significantly blocked by the mask, resulting in an ineffective accuracy for face recognition system. To be able to identify a person while wearing a face mask, an alternative system has to be considered. There have been several studies in ear recognition system in which an impressive accuracy is obtained. In this work, ear recognition system with the AMI ear database is studied.The feature in terms of histogram of oriented gradients (HOG) is used, and the support vector machine (SVM) is adopted for classification process. To increase the recognition accuracy, ear images are preprocessed by adjusting the sharpness level. It is found that using the concatenated HOG features from the sharpened RGB and HSV images, a promising average recognition accuracy of 86% and the standard deviation of 2.91% are obtained. © 2021 IEEE.

Automatic Rephrasing of Transcripts-based Action Items

The automated transcription of spoken language, and meetings, in particular, is becoming more widespread as automatic speech recognition systems are becoming more accurate. This trend has significantly accelerated since the outbreak of the COVID-19 pandemic, which led to a major increase in the number of online meetings. However, the transcription of spoken language has not received much attention from the NLP community compared to documents and other forms of written language. In this paper, we study a variation of the summarization problem over the transcription of spoken language: given a transcribed meeting, and an action item (i.e., a commitment or request to perform a task), our goal is to generate a coherent and self-contained rephrasing of the action item. To this end, we compiled a novel dataset of annotated meeting transcripts, including human rephrasing of action items. We use state-of-the-art supervised text generation techniques and establish a strong baseline based on BART and UniLM (two pretrained transformer models). Due to the nature of natural speech, language is often broken and incomplete and the task is shown to be harder than an analogous task over email data. Particularly, we show that the baseline models can be greatly improved once models are provided with additional information. We compare two approaches: one incorporating features extracted by coreference-resolution. Additional annotations are used to train an auxiliary model to detect the relevant context in the text. Based on the systematic human evaluation, our best models exhibit near-human-level rephrasing capability on a constrained subset of the problem. © 2021 Association for Computational Linguistics

Real-time Face Recognition System for Remote Employee Tracking

During the COVID-19 pandemic, most of the human-to-human interactions have been stopped. To mitigate the spread of deadly coronavirus, many offices took the initiative so that the employees can work from home. But, tracking the employees and finding out if they are really performing what they were supposed to turn out to be a serious challenge for all the companies and organizations who are facilitating “work from home.” To deal with the challenge effectively, we came up with a solution to track the employees with face recognition. We have been testing this system experimentally for our office. To train the face recognition module, we used FaceNet with KNN using the Labeled Faces in the Wild (LFW) dataset and achieved 97.8% accuracy. We integrated the trained model into our central system, where the employees log their time. In this paper, we discuss in brief the system we have been experimenting with and the pros and cons of the system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.