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.

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).

Use of Deep Learning and Blockchain Technologies in Healthcare Industry

This editorial summarises the special issue entitled "Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems”, which deals with the intersection and use of deep learning and blockchain technologies in the healthcare industry. This special issue consists of eleven scientific articles. © 2023 by the author(s). Licensee Prague University of Economics and Business, Czech Republic.

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.

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

Completely Contactless and Online Finger Knuckle Identification for Real World Applications

Completely contactless and at-a-distance personal identification provides enhanced user convenience, and improved hygiene and is highly sought under the COVID-19 pandemic. This paper proposes an accurate and generalizable deep neural network-based framework for the ‘completely’contactless finger knuckle identification. We design and introduce a new loss function to enable a fully convolutional network to more effectively learn knuckle features that are imaged under at-a-distance imaging. A ‘completely’contactless system also requires efficient online finger knuckle detection capabilities. This paper, for the first time in our knowledge, develops and introduces accurate capabilities to efficiently detect and segment finger knuckle patterns from images with complex backgrounds as widely observed in real-world applications. We introduce angular loss to accurately predict oriented knuckle patterns and incorporate into our framework. Experimental results presented in this paper on five different public databases, using challenging protocols and cross-database performance evaluation, illustrate outperforming results and validate the effectiveness of the proposed framework for completely contactless applications. 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.

Person Identification and Gender Classification Based on Vision Transformers for Periocular Images

Simple SummaryThe idea of identifying persons using the fewest traits from the face, particularly the area surrounding the eye, was carried out in light of the present COVID-19 scenario. This may also be applied to doctors working in hospitals, the military, and even in certain faiths where the face is mostly covered, except the eyes. The most recent advancement in computer vision, called vision transformers, has been tested for the UBIPr dataset for different architectures. The proposed model is pretrained on an openly available ImageNet dataset with 1 K classes and 1.3 M pictures before using it on the real dataset of interest, and accordingly the input images are scaled to 224 × 224. The PyTorch framework, which is particularly helpful for creating complicated neural networks, has been utilized to create our models. To avoid overfitting, the stratified K-Fold technique is used to make the model less prone to overfitting. The accuracy results have proven that these techniques are highly effective for both person identification and gender classification.AbstractMany biometrics advancements have been widely used for security applications. This field's evolution began with fingerprints and continued with periocular imaging, which has gained popularity due to the pandemic scenario. CNN (convolutional neural networks) has revolutionized the computer vision domain by demonstrating various state-of-the-art results (performance metrics) with the help of deep-learning-based architectures. The latest transformation has happened with the invention of transformers, which are used in NLP (natural language processing) and are presently being adapted for computer vision. In this work, we have implemented five different ViT- (vision transformer) based architectures for person identification and gender classification. The experiment was performed on the ViT architectures and their modified counterparts. In general, the samples selected for train:val:test splits are random, and the trained model may get affected by overfitting. To overcome this, we have performed 5-fold cross-validation-based analysis. The experiment's performance matrix indicates that the proposed method achieved better results for gender classification as well as person identification. We also experimented with train-val-test partitions for benchmarking with existing architectures and observed significant improvements. We utilized the publicly available UBIPr dataset for performing this experimentation.

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.

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.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification, which helps prevent infections like COVID-19 from spreading. Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses, replayed the video, and print attacks. The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness. Seven assorted feature creation ways are studied in the presented solutions, and these created features are explored for the training of eight distinct machine learning classifiers and ensembles. The predicted iris liveness identification variants are evaluated using recall, F-measure, precision, accuracy, APCER, BPCER, and ACER. Three standard datasets were used in the investigation. The main contribution of our study is achieving a good accuracy of 99.18% with a smaller feature vector. The fragmental coefficients of Haar transformed iris image of size 8 ∗ 8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size (64 features). Random forest gave 99.18% accuracy. Additionally, conduct an extensive experiment on cross datasets for detailed analysis. The results of our experiments show that the iris biometric template is decreased in size to make the proposed framework suitable for algorithmic verification in real-time environments and settings. © 2023 Tech Science Press. All rights reserved.

Machine Learning based Automated Product Billing and Inventory

Due to the COVID-19 pandemic restrictions were imposed to stop the spread of the virus. As a result, the shopping malls, retail stores and grocery stores had to be shut down leading to significant losses. Though online shopping is always an option, buying daily groceries online is not a feasible option due to delivery times. As the world is now recovering from the pandemic, people have now started visiting malls, retail stores and other places for buying grocery items. In such situation it then becomes very crucial to help people shop more efficiently to reduce the buying time. This will help to keep the crowd under control without jeopardizing the safety and social distance norms. The major problem with most top retail stores is that the customer must wait in long queues after buying their products for getting them billed and payment. This puts pressure on the management and billing staff as well as makes the process of shopping a time consuming and unpleasant experience for the customer. The other issue is that most retail stores waste a significant area of their shop in setting up the billing counters and furthermore also must recruit people for running the billing counters. This project proposes a way of solving the above-mentioned problems and issues with the help of a smart shopping cart. A cart with built in billing and product scanning system which will prepare the bill as the customer is shopping, thus saving customer's valuable time, area of the shop and eliminating the need for recruiting employees for billing. © 2022 IEEE.

Baseline study of COVID-19 and biometric technologies

COVID-19 is a pathogenic viral infection caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), which emerged in Wuhan, China in December 2019 and has spread to several countries of the world resulting in economic hardship and travel restrictions. This paper presents findings on the baseline study of COVID-19 and biometric technologies. The study included succinct discussions on biometric technologies prior to and since outbreak of COVID-19 and an online survey involving 2438 randomly selected individuals via questionnaire that centered on the world s economy with daily application of biometric technologies. The questionnaire featured indices on biometric technologies and global security, the rating of each biometric mode in the global security performance scale among others. Analysis of data from the survey established the paradigm shift in biometric applications from contact-based to contact-free since the outbreak of the disease, low risk level between COVID-19 and biometric technologies and diminishing cash flow in biometric market. © 2022 Information Resources Management Association. All rights reserved.

Rain Drop Service and Biometric Verification Based Blockchain Technology for Securing the Bank Transactions from Cyber Crimes Using Weighted Fair Blockchain (WFB) Algorithm

Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage. [ FROM AUTHOR] Copyright of Cybernetics & Systems 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.)

Image Similarity between Masked and Unmasked Face for Consumer Electronics Applications

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.

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.

CoviChain: A Blockchain Based COVID-19 Vaccination Passport

Vaccination passports are being issued by governments around the world in order to open up their travel and hospitality sectors. Civil liberty campaigners on the other hand argue that such mandatory instruments encroach upon our fundamental right to anonymity, freedom of movement, and are a backdoor to issuing "identity documents” to citizens by their governments. We present a privacy-preserving framework that uses two-factor authentication to create a unique identifier that can be used to locate a person's vaccination record on a blockchain, but does not store any personal information about them. Our main contribution is the employment of a locality sensitive hashing algorithm over an iris extraction technique, that can be used to authenticate users and anonymously locate vaccination records on the blockchain, without leaking any personally identifiable information to the blockchain. Our proposed system allows for the safe reopening of society, while maintaining the privacy of citizens. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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

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

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.