ABSTRACT
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.
ABSTRACT
This research develops a contactless and secure access control system based on face recognition and body temperature measurement. This research aims to establish a security system that also fulfills health protocols for COVID-19 spreading, in this case, the limitation of physical contact. The PRESENT algorithm, a lightweight block cipher encryption-decryption algorithm, is implemented to keep the transmitted data safe. The face recognition method consists of the Viola-Jones face detection algorithm and LBPH face recognition algorithm. The body temperature is measured using a contactless sensor. The performance tests show the accuracies of recognizing faces are 68% under 198 Lux lighting and 52% under 105 Lux lighting. The precision of measuring body temperature using the sensor reaches 98,85%. Based on the sniffing attack test of the system, the encrypted data transmitted from the system to the web-based database is safe from attackers. Besides the face spoofing attack tests, the system will not authenticate attackers with face photos or face videos. © 2022 IEEE.
ABSTRACT
Because of coronavirus variants, it is necessary to pay attention to epidemic prevention measures in the cultivation or product packaging processes. In addition to giving customers more peace of mind when using the products, it also ensures that operators wear masks, work clothes and gloves in the work area. This paper constructs an access control system for personnel epidemic prevention monitoring, which uses IoTtalk [1] to connect IoT devices (such as magnetic reed switches, intelligent switches, RFID readers, and RFID wristbands), utilizes RFID for personnel identification, and employs real-time streaming protocol [2] to take the image of IP Cam for YOLOv4 [3] identification program. The identification program detects whether the personnel is indeed wearing the required equipment. If the personnel is not wearing the required device, the detector will trigger a push broadcast system constructed by LINE Notify to inform the operator for processing. Moreover, we developed an emergency entry mechanism;if an emergency happens, the personnel can trigger the emergency door opening by swiping the card multiple times within a specified time. This function allows the person to enter without wearing the required equipment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Face recognition has become essential as a convenient biometric-based solution for a plethora of different consumer electronics applications, including access control systems, intelligent environments, smartphone authentication systems and so on. Early in 2020, the COVID-19 pandemic caused the widespread use of face masks, which become essential for containing the outbreak. The masks cause a visible alteration in facial appearance, covering almost the 50% of the human face. In this work, an image similarity technique is applied to assess the difference between two images of the same face wearing or not wearing a face mask. Cosine Similarity measure-based Algorithm (CSA) was used to objectively infer the difficulties that modern facial recognition algorithms, based on deep learning techniques, encounter when dealing with a masked face. © 2023 IEEE.
ABSTRACT
This article presents an application of sound identification using machine learning techniques. Identification for access control system such as an entering-exiting turnstile in the building gate is still required for people's working lives, in general. However, under COVID-19 pandemic, a new norm or New-Normal has emerged to reduce and prevent the spread of the COVID-19 virus. Sound identification system is considered as a system of identification/authentication without any direct contact between the people and the system equipment. Therefore, in this work, a sound identification system is studied and developed. To analyze and feature-extract the sound from a pre-processed human voice, MFCC (Mel Frequency Cepstral Coefficient) technique is adopted. For identification process, the feature vector obtained from MFCC is sent to 3 different popular machine learning techniques.;namely, CNN (Convolutional Neural Network), GMM (Gaussian Mixture Models), and SVM (Support Vector Machine). This results in sound authentication with true positive accuracy of 87.90%, 52.98%, and 41.37%, respectively, and true negative accuracy of 52.98%, 35.12%, and 90.48%, respectively. The best true positive and true negative accuracies are from CNN and SVM, respectively. The results can be further applied in sound identification system. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).
ABSTRACT
In the current pandemic, which started on last 2020, there are many limitations we should do for reducing the spread of the virus named covid19. Keeping the distance from others is the way to prevent infected covid19. Contactless technology should be developed further, for example, in this paper. Access Control System by Face recognition and Biometrics is one reason to be developed. OpenCV is a tool that will use to build face recognition. This system is only intended for small office areas or smart homes. Currently, only five registered users. The data registration process is carried out outside the system or only loads an algorithm model. This system will also provide information on social media, Whatsapp, if a known person enters the room or an unknown person. © 2022 IEEE.
ABSTRACT
Nowadays, the COVID-19 pandemic has changed our lives. Some biosecurity measures have been implemented, including the use of face masks and the detection of body temperature;however, there are a lot of outbreaks, and the world cannot overcome this illness. In multiple cases, it is very difficult to measure the temperature and verify the correct use of face masks in everyone. Therefore, this paper proposes a real-time access control system based on body temperature detection and the correct use of face masks. This system uses a Raspberry Pi 4, which integrates temperature measurement using a thermal imager, the detection of the correct use of face masks using Convolutional Neural Networks (CNN), with a model built based on TensorFlow and MobileNetV2 that works on the video obtained from a thermographic camera using OpenCV and the Real Real-Time Streaming Protocol (RSTP). The system includes four modules: body temperature detection, processes, access, and visual interface. As a result, the access control system establishes six classification cases: high temperature and low temperature in faces without a mask, with an incorrectly placed mask, and with a correctly placed mask. The results show a system performance greater than 95% in all cases with a neural network model trained with a learning rate of 10E-4 and 15 epochs. © 2022 IEEE.
ABSTRACT
The pandemic experienced in the last two years in the world has led people to be much more careful in their social relations, keeping their social distance and using hygienic prevention measures. However, when it is necessary to enter crowded closed environments, people feel insecure and are more afraid of contagion. This situation leads to the need for measures to control access to public places in order to prevent infection and to reinforce people’s confidence. Various devices and solutions exist to control access, ranging from card-based identification to biometric sensors. However, they have shortcomings detected during the pandemic, such as the need to touch elements or the types of computing used, which can compromise security and/or response times. The solution proposed in this article integrates the best of these by incorporating facial recognition using neural networks, the presence or absence of a mask and medical Internet of Things (IoT) devices to monitor pulse, blood oxygen and body temperature. All this technology is used to check whether the person’s access is safe for them and others. The data collection process in this system has proven to be efficient thanks to fog computing, which reduces latency times and prevents the user’s data from being accessed by third parties while maintaining their privacy. © 2022, Springer Nature Switzerland AG.
ABSTRACT
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.
ABSTRACT
Wearing protective masks has become commonplace in several countries as a result of the epidemic connected to the Covid-19 virus. Wearing a mask obscures a considerable part of the face, making some facial recognition techniques difficult to complete and obstructing the operation of various identifying systems, such as access control systems. In this paper, we offer an original approach that allows many face recognition systems to continue to identify persons even when wearing protective masks. The proposed approach is mainly based on the prior use of skin detection techniques. We validated our method using the Eigenfaces method by the FEI database, which we supplemented with faces wearing protective masks. The evaluation results of our approach are very satisfactory. © 2022 Elsevier B.V.. All rights reserved.
ABSTRACT
COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization. © 2022 IEEE.
ABSTRACT
Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart university is to adopt technology to help with teaching and learning, especially during the Covid-19 pandemic. In this paper, a smart attendance system using face recognition algorithms with deep learning is proposed and used in the university's classroom. Instead of calling names to confirm the identity of students, our system does it automatically. The system was tested in 3 scenarios, namely, in online classes, in on-site classes, and in problematic cases using a standard dataset. The performances of the 3 scenarios were compared in the experiment in terms of precision, recall, F1 score, and percentage accuracy. Our result revealed that in online classes the recognition accuracy is as high as 100%. The implemented system is inexpensive and practical. The application can be used on any portable device such as tablets or smartphones. History viewing, multiple subjects handling, and file exporting features are also incorporated into the system. © 2022 IEEE.
ABSTRACT
During the period of prevailing unsettled COVID pandemic, the countries and states started to plan reopening during which necessitates the non-contact temperature evaluation gadgets as a part of a preliminary look at access points to identify the humans with elevated body temperatures. Despite the utilization of these devices, temperature assessment restricted the impact on lowering the spread of COVID-19. Non-contact temperature measuring devices are used to measure the temperature of any person. Detection of a high temperature is one huge manner to pick out a person who might also have COVID-19 contamination. In this project, a room environment is created in which certain precautions are taken. A laser diode and receiver are used to detect the entrance of a person, and the system also detects the body temperature of the entering person. If the temperature is less than a threshold temperature entry for the person is permitted or else the entry is denied. This system also has a feature where it permits only a pre-determined number of persons inside the room. It also facilities to view the allowed temperature, the number of people to be allowed in the room and the number of people present actively using a Bluetooth App. This system aimed to be useful to combat the spread of COVID infections. © 2022 IEEE.
ABSTRACT
Aiming at the problem of long queuing temperature and low efficiency during the period of COVID-19, an intelligent temperature measurement and access control system is designed. It is widely used in enterprises, institutions, scenic spots, commercial areas and other places with large traffic volume. The main control system adopts stm32f407zgt6 embedded chip, hc-sr501 human body infrared sensor is sensitive to human body proximity, and starts the temperature measurement system. Using hc-sr04 ultrasonic sensor to measure people's height and adjust the height of mlx9064esf temperature probe, the system has the characteristics of efficient real-time temperature monitoring, and improves the detection efficiency in places with large flow of people. The experimental results show that the system has good practical application effect. © 2022 IEEE
ABSTRACT
This paper presents a review on the state-of-the-art of the access control systems based on face recognition. The review reveals the following: i) More than fifty percent of the related contributions have been published in the last five years. ii) The most used techniques to achieve the face recognition are neural networks, principal component analysis, local binary pattern, and linear discriminant analysis. These techniques have been applied mostly to improve the performance of the recognition accuracy or recognition rate and less for addressing variations in illumination, face spoofing, information security, privacy, face occlusion, computational time, classification performance, small sample size, and recognition with low-resolution images, pose variations, and expression changes. iii) Other several techniques, including Viola-Jones, hidden Markov model, and Gaussian mixture model, have been less used to deal with the aforementioned problems (except recognition with low-resolution images, pose variations, and expression changes) and recognition with retouched or rotated images. iv) New challenges in the face recognition-based control systems appeared due to the occlusion of the faces with masks by COVID-19. Also, open challenges and future work where artificial intelligence could be harnessing are given. © 2021 IEEE.