An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. © 2023 IEEE.

Citizen empowerment through smart surveillance: evidence from Indian smart cities

PurposeCitizens often perceive surveillance by government authorities as oppressive and, hence, demonstrate reluctance in value co-creation from such services. This study aims to investigate the challenges and benefits of citizen empowerment through technology-driven surveillance or "smart surveillance.”Design/methodology/approachGuided by Dynamic Capability theory, the authors conduct in-depth interviews with officers in-charge of surveillance in smart cities. Given the contemporary advancements, this approach allows a retrospective and real-time understanding of interviewees' experiences with smart surveillance.FindingsThe authors develop five propositions for citizen empowerment through smart surveillance to summarize the findings of this study.Research limitations/implicationsThis study advances the relevance of Dynamic Capability in public administration.Practical implicationsSmart city authorities and policymakers may leverage the insights provided in this study to design appropriate policies for smart surveillance.Originality/valueThe authors find that factors such as digital technology and infrastructure, information management, skill divide and perceived return on investment may influence citizen empowerment through smart surveillance.

Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region

In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.

Integrating Technology and Expertise

Phase I/II study in cancer patients showcases eClinical effectiveness Complex study designs are fast becoming the norm-but integrated clinical trial technology can make even the more complicated of data collection requirements a breeze. Solution The sponsor selected IDDI as its biometric partner, and we provided our fully integrated eClinical suite, which features electronic data capture (EDC) and RTSM, as well as data collection, data cleaning, and data analysis. [...]resources were planned on almost real-time trial status, thus reducing timeand resource-wasting activities.

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.

Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

Technology-Driven Implementation of Smart Entrances in Public Places during the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.

Cardless Cash Withdrawal Using Palm Vein Technology

Generally, the easiest way to withdraw money from your bank account is by using an Automated Teller Machine (ATM). The user can withdraw the money by inserting their card into the slot on the machine, and then entering a four-digit Personal Identification Number (PIN) to complete the transaction process. Similarly, some banks adopted the method of using a One Time Password (OTP) to complete the transaction process to make it more secure. With the recent advancements in technology, there are many new methods that can be used for withdrawing money from ATMs, like cardless cash withdrawal or using one's biometrics. But, due to the recent COVID-19 pandemic, we refrain from using things that are not sanitized properly. People started avoiding going to the ATMs since hygiene was a major concern during the pandemic. Also, due to the constant hand washing and the use of sanitizers, the use of conventional biometrics was not efficient. As a result, the idea of using a method that is contact-less and is also more secure emerged, i.e., the palm vein technology. The palm vein technology uses a person's vein pattern, which is unique to everyone and can help us achieve better results with greater accuracy. The paper proposes a concept of using a person's vein pattern as a method of contact-less authentication. It is an extremely safe verification procedure because no two people in the world, not even identical twins, can have the same palm vein structure or pattern. Additionally, it is more secure because it is nearly impossible to replicate the palm vein pattern. © 2022 IEEE.

Biometrics in Tourism: Issues and Challenges

In tourism, biometrics have become a major technology application for user authentication in premises such as hotels and airports. The main reasons of utilizing biometrics for access control are convenience, high security, and reliability. Biometrics refer to any human trait, for instance, fingerprint, retina, palm print, and voice, which is used to establish an identity. It is already used extensively in many airports worldwide to facilitate automated identity verification, while the backbone is linked to police criminal databases, which enables identification of possible terrorists, victims of human trafficking, or tourists with past convictions. Additionally, many hotels have substituted traditional door locks or card access with biometrics for its convenience. However, there are some concerns associated with biometric applications, for instance, information privacy, physical privacy, and contamination fears. Tourists worry whether the storage mechanism of biometric data is safe from hacking and leakage to terrorist organizations. If biometric data were compromised, tourists would have to surrender different biometric identifiers because biometric features are irrevocable (permanent). Further, fear of contamination can be much more apparent on touch-based biometric applications such as fingerprint scanners. This concern is aggravated especially during pandemic outbreaks such as COVID-19, Ebola, and SARS. In a nutshell, this chapter aims to provide a holistic perspective on potential issues of biometrics in the tourism industry. © Springer Nature Switzerland AG 2022.

Deep convolutional neural network-based effective model for 2D ear recognition using data augmentation

In the pandemic of COVID-19, identifying a person from their face became difficult due to wearing of mask. In regard to the given circumstances, the authors have remarkably put effort on identifying a person using 2D ear images based on deep convolutional neural network (CNNs). They investigated the challenges of limited data and varying environmental conditions in this regards. To deal with such challenges, the authors developed an augmentation-based light-weight CNN model using CPU enabled machine so that it can be ported into embedded devices. While applying data augmentation technique to enhance the quality and size of training dataset, the authors analysed and discussed the different augmentation parameters (rotation, flipping, zooming, and fill mode) that are effective for generating the large number of sample images of different variability. The model works well on constrained and unconstrained ear datasets and achieves good recognition accuracy. It also reduces the problem of overfitting. [ FROM AUTHOR] Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

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.

Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software. © 2022 IEEE.

NF-Heart: A Near-field Non-contact Continuous User Authentication System via Ballistocardiogram

The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances.

Workout Detection by Wearable Device Data Using Machine Learning

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.

Face mask detection using deep convolutional neural network and multi-stage image processing

• A customized image dataset is built for research on face mask detection. • The dataset is manually labeled to provide high annotation accuracy. • For Face mask detection customized CNN with multi-step image processing is used. • The performance of the proposed CNN is compared with YOLO v3 and Faster R-CNN. • Two publicly available datasets including MAFA and MOXA used for validation. Face mask detection has several applications including real-time surveillance, biometrics, etc. Face mask detection is also useful for surveillance of the public to ensure face mask wearing in public places. Ensuring that people are wearing a face mask is not possible with monitoring staff;instead, automatic systems are a much better choice for face mask detection and monitoring to help manage public behaviour and contribute to restricting the outbreak of COVID-19. Despite the availability of several such systems, the lack of a real image dataset is a big hurdle to validating state-of-the-art face mask detection systems. In addition, using the simulated datasets lack the analysis needed for real-world scenarios. This study builds a new dataset namely RILFD by taking real pictures using a camera and annotating them with two labels (with mask, without mask) which are publicly available for future research. In addition, this study investigates various machine learning models and off-the-shelf deep learning models YOLOv3 and Faster R-CNN for the detection of face masks. The customized CNN models in combination with the 4 steps of image processing are proposed for face mask detection. The proposed approach outperforms other models and proved its robustness with a 97.5% of accuracy score in face mask detection on the RILFD dataset and two publicly available datasets (MAFA and MOXA). [ FROM AUTHOR] Copyright of Image & Vision Computing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

This work was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number P50NS108676 and Moore Foundation

The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances. © 2023 ACM.

Cyber-physical customer management for internet of robotic things-enabled banking

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymise the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations. Author

A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade

Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.

Improving Masked Face Recognition Using Dense Residual Unit Aided with Quadruplet Loss

All over the world, people are wearing face masks and practising social distancing to protect themselves against the Coronavirus disease (COVID-19). The need for contactless biometric systems has increased to avoid the common point of contact. Among contactless biometric systems, facial recognition systems are the most economical and effective ones. Conventional face recognition systems rely heavily upon the facial features of the eyes, nose, and mouth. But due to people wearing face masks, the important facial features of the nose and mouth get hidden under the mask, resulting in degraded performance by the facial recognition systems on masked faces. In this paper, we propose a Dense Residual Unit (DRU) aided with Quadruplet loss on top of existing facial recognition systems. This solution tries to unveil the masked faces by producing embeddings for masked faces, which are similar to embeddings of unmasked faces of the same identity but different from embeddings of different identities. We have evaluated our method using two pre-trained facial recognition models' backbones, i.e. Resnet-101 and MobileFaceNet, and upon two datasets, among them, one is a real-world dataset, i.e. MFR2, and one is a simulated masked face dataset i.e. masked version of LFW. We have achieved improvement in the performance of masked face recognition in terms of False Match Rate, False Non-Match Rate, Fisher Discriminant ratio, and Equal Error Rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine learning-based iris liveness identification using fragmental energy of cosine transformed iris images

Iris biometric identification provides a contactless authentication preventing the spread of COVID-19 like diseases. These systems are made vulnerable and unsafe because of the spoofing attacks attempted with the help of contact lenses, video replays and print attacks. The paper proposes the iris liveness detection method to mitigate spoofing attacks, taking fragmental coefficients of cosine transformed iris image to be used as features. Seven variants of feature formation are considered in experimental validations of the proposed method, and the features are used to train eight assorted machine learning classifiers and ensembles for iris liveness identification. Recall, F-measure, precision and accuracy are used to evaluate performances of the projected iris liveness identification variants. The experimentation carried out on four standard datasets have shown better iris liveness identification by the fragmental coefficients of cosine transformed iris image with size 4 ∗ 4 using random forest algorithm having 99.18% accuracy immediately followed by an ensemble of classifiers. Copyright © 2023 Inderscience Enterprises Ltd.