Biometric Technology Research Articles

Directions of emerging structural changes in the use of biometric data in Russia

Objective: to identify the emerging structural changes in the use of biometrics in Russia, namely the multiple growth of market participants and brands, the multiple growth of the number of biometrics samples in a single biometric system, and the multiple growth of market volume and institutional design of biometrics use in Russia. Methods: general scientific, abstract-logical, dialectical, phenomenological methods, observation, description. Results: the article describes approaches to the use of biometric data in some regions of the world, identifies the risks and potential benefits of wider use of biometric data, summarizes the main trends in the use of biometric personal data in Russia, presents a comprehensive author’s approach to the analysis of structural changes in the economy, taking as an example thedevelopment of solutions using biometric personal data for the identification of individuals. Scientific novelty: the author proposes to consider the formation of structural shift by identifying its four components and qualitatively assessing their level of formation (low, medium, and high); shows the formation of macro-shifts from micro-level initiatives and sequential passage of the stages of specific structural changes, complex micro-shifts, institutionalization andconsolidation of new practices. In addition, the process of implementation of structural shifts from the macro- to the micro-level is traced, which consists in the changes more widely covering economic agents compared to the stage of a structural shift formation. All this constitutes a comprehensive author’s approach to the regularities of the structural shifts developmentin the economy. Practical significance: clarification of interrelations between the processes of structural change will improve the programs of socio-economic development of regions, industrial policy for medium- and high-tech industries through the use of modern technological methods of identification and authentication of customers and employees with biometrics. It may also create a basis for deepening the existing knowledge in the development of biometric technologies in Russia and expanding the practice of their application.

Biometric Technology Based on Wireless Sensing: Principles, Applications, and Challenges

With the rapid advancement of computer technology, image processing, and artificial intelligence, biometric identification technology has seen widespread application, gradually permeating areas such as national security, public security, and home security. However, traditional biometric technologies exhibit certain limitations. In contrast, biometric identification based on wireless sensing offers distinct advantages, including non-contact operation and strong concealment, making it a promising alternative in various security applications. This paper provides a comprehensive overview of the history and current state of biometric technology, alongside the development background of wireless sensing technology. It details the principles of wireless sensing-based biometric identification, covering essential characteristics of wireless signals, and related technologies such as RFID, Wi-Fi, Bluetooth, and millimeter wave technology, as well as relevant signal processing algorithms. Furthermore, the paper analyzes the application of this technology in areas like human posture and motion recognition and physiological feature recognition, supported by practical case studies. Finally, it addresses the environmental dependencies, data privacy, and security concerns associated with this technology, while offering insights into future development directions.

Abstracting Injustice

In this paper, we explore how the neorepublican concepts of domination and antipower can contribute to the surveillance studies literature and a more democratic and participatory approach to technology development and deployment within the criminal justice system. We frame the neorepublican approach as an alternative to the predominant liberal paradigm, arguing that normative surveillance studies scholarship should emphasize the dominating potential of surveillance practices rather than merely trying to limit actual interference in peoples’ lives. To illustrate, we focus on the use of surveillance technologies that capture images of individuals within the US criminal justice system for recognition and/or identification. Facial or other biometric recognition technologies (FRTs) are increasingly built on artificial intelligence and machine learning algorithms (“AI”). Often seen as a faster, more accurate, and less labor-intensive alternatives to human cognition, AI-powered biometric and facial recognition and other image capture technologies have become widely used within public law enforcement agencies around the world. The deployment of these technologies within the US criminal justice system has produced significant forms of injustice, including faulty identification and the subsequent arrest, detention, and incarceration of innocent individuals. These forms of data injustice are often opaque, hidden behind secretive law enforcement practices or commercial secrecy agreements. We draw from neorepublican conceptions of domination and antipower to frame this legal and technological opacity as an abstraction of injustice. We argue that handing important criminal justice decision-making over to code and algorithms designed, owned, and maintained by private interests exacerbates the potential for the public deployment of unjust systems that subject individuals and communities to unwarranted, arbitrary, and uncontrolled state power. Such government interference represents clear forms of data injustice and domination.

Supporting and Humiliating Dignity with Biometric Technologies: An Affordance Perspective

Supporting and Humiliating Dignity with Biometric Technologies: An Affordance Perspective

Intelligent Fingerprint Recognition System Using Machine Learning

Secure authentication relies heavily on fingerprint recognition systems; however, conventional techniques are hindered by differences in image quality and possible security breaches. This study suggests a novel method for improving fingerprint recognition accuracy and robustness that makes use of machine learning techniques. By utilizing convolutional neural networks' (CNNs') capacity to deduce intricate patterns and representations from fingerprint photos, we investigate the use of CNNs for feature extraction and matching. Comparing our experimental results against conventional approaches, we find considerable improvements in accuracy and false acceptance rates on benchmark datasets. By advancing biometric authentication technologies, this research helps to improve security and dependability for a range of applications in law enforcement, access control, and personal identification.

Investigation on airline passengers’ behaviors for biometric boarding technology: a combined application of TAM, AST, and BRT

ABSTRACT With the application and promotion of digital intelligence technology in various industries, biometric boarding technology (BBT) for airline passengers has been advancing. This study proposed a conceptual model incorporating the technology acceptance model (TAM), agenda setting theory (AST), and behavioral reasoning theory (BRT) in the BBT context. 331 valid cases collected from South Korea were used to estimate the efficiency of the proposed framework in understanding airline passengers’ psychological reactions to BBT use and their future behaviors. This study examined how various variables (specific reasons and concerns about BBT use, perceived ease of use and usefulness of BBT, media exposure, awareness, engagement, and attitudes) influence behavioral intentions relating to BBT. Research findings provide major insights into how to use BBT to enhance passengers’ smart travel experiences and integrate the resilience of smart tourism development under the sharing economy.

DESAFIOS DE SEGURANÇA NAS INSTITUIÇÕES FINANCEIRAS DIGITAIS: ATAQUES MALWARE E MECANISMOS DE SEGURANÇA

Financial institutions face increasing challenges related to information security due to the intensification of cyber attacks, particularly threats such as malware and ransomware. This study aims to critically analyze these risks and evaluate the effectiveness of current technological tools and current regulations in preserving the supervision and confidentiality of digital financial systems. To this end, we adopted an exploratory and qualitative approach, based on a comprehensive literature review. Research has revealed that the adoption of artificial intelligence, especially through neural networks and machine learning algorithms, has proven effective in identifying attacks and containing malicious behavior in real time. Intrusion detection and prevention systems (IDS/IPS), combined with multifactor authentication protocols and biometric technologies, have brought benefits to the identity verification and fraud protection process. It is concluded that effectively combating malware attacks requires the adoption of advanced security mechanisms, such as machine learning, multifactor authentication and blockchain, integrated with artificial intelligence. This combination enables rapid detection and response to suspicious behavior, strengthening data protection in digital financial institutions.

Privacy Preserving Technologies

As the digital world continues to undergo fast change, the need of privacy-preserving technology has increased significantly in order to protect personal data from being accessed and used inappropriately by unauthorised parties. Blockchain technology, the Internet of Things (IoT), and biometrics are all investigated in this study as potential key components for improving privacy and security. A robust framework for securing transactions and data exchanges, ensuring transparency and integrity, is provided by blockchain technology, which features a decentralised and immutable data ledger. Concerns about privacy are raised as a result of the huge volume of sensitive data that is created and transferred by the Internet of Things (IoT), which connects a wide variety of devices. The purpose of this study is to investigate the application of blockchain technology for the purpose of securing Internet of Things settings, with the goal of preserving data integrity and user privacy. Moreover, biometrics, which are a kind of identification and verification that makes use of distinctive physical or behavioural features, are an essential component in the process of enhancing access control and personalised security. Within the scope of this article, we address recent developments in biometric technologies and how they might be used with blockchain technology to provide systems that are both safe and enhance privacy. This study illustrates the potential of various technologies to create a complete framework that protects individuals' privacy and solves the difficulties and possibilities that are presented by the digital age. This potential is shown by analysing the intersection of many technologies.

Beyond the Fingerprint: Exploring the Bibliometric Landscape of Emerging Synthetic Biometrics with a Focus on Synthetic Signatures

Purpose: This paper aims to conduct a comprehensive bibliometric analysis focusing on synthetic biometrics, particularly synthetic signatures, as an emerging alternative to traditional biometric methods such as fingerprints and iris scans. The study explores the research landscape to identify trends, influential studies, and emerging technologies in this innovative field. Methodology: The research employs bibliometric analysis techniques to systematically map the academic discourse surrounding synthetic biometrics, with a specific emphasis on synthetic signatures. Bibliometric methods include citation analysis, co-citation analysis, and cluster analysis to uncover patterns, connections, and trends within the research literature. These methods provide a retrospective insight into the progression of synthetic signature research and its scholarly impact. Findings/Results:The findings reveal several key insights: Emerging Trends: Synthetic signatures are increasingly recognized for their potential in enhancing security and identification systems beyond traditional biometrics. Influential Studies: Specific studies and authors are identified as pivotal in shaping the discourse and development of synthetic signature technologies. Technological Advancements: The analysis highlights advancements in synthetic biometrics, showcasing how these technologies are evolving to meet the challenges of robust and adaptable verification methods. Challenges and Opportunities: The study identifies both potential and challenges associated with synthetic signatures, providing a nuanced understanding of their feasibility and application in real-world scenarios. Originality/Value: This paper contributes to the existing literature by offering a structured analysis of the bibliometric landscape of synthetic biometrics, particularly synthetic signatures. By mapping out the academic discourse and identifying key trends and technologies, it provides valuable insights into the potential and challenges of this innovative field. The findings are expected to guide future research directions and policy considerations in the development and adoption of synthetic biometric technologies. Paper Type: Review Paper.

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.

EEG Data Augmentation Method for Identity Recognition Based on Spatial–Temporal Generating Adversarial Network

Traditional identity recognition methods are facing significant security challenges due to their vulnerability to leakage and forgery. Brainprint recognition, a novel biometric identification technology leveraging EEG signals, has emerged as a promising alternative owing to its advantages such as resistance to coercion, non-forgeability, and revocability. Nevertheless, the scarcity of high-quality electroencephalogram (EEG) data limits the performance of brainprint recognition systems, necessitating the use of shallow models that may not perform optimally in real-world scenarios. Data augmentation has been demonstrated as an effective solution to address this issue. However, EEG data encompass diverse features, including temporal, frequency, and spatial components, posing a crucial challenge in preserving these features during augmentation. This paper proposes an end-to-end EEG data augmentation method based on a spatial–temporal generative adversarial network (STGAN) framework. Within the discriminator, a temporal feature encoder and a spatial feature encoder were parallelly devised. These encoders effectively captured global dependencies across channels and time of EEG data, respectively, leveraging a self-attention mechanism. This approach enhances the data generation capabilities of the GAN, thereby improving the quality and diversity of the augmented EEG data. The identity recognition experiments were conducted on the BCI-IV2A dataset, and Fréchet inception distance (FID) was employed to evaluate data quality. The proposed method was validated across three deep learning models: EEGNET, ShallowConvNet, and DeepConvNet. Experimental results indicated that data generated by STGAN outperform DCGAN and RGAN in terms of data quality, and the identity recognition accuracies on the three networks were improved by 2.49%, 2.59% and 1.14%, respectively.

Enhancing Biometric Security with Bimodal Deep Learning and Feature-level Fusion of Facial and Voice Data

Recent research in biometric technologies underscores the benefits of multimodal systems that use multiple traits to enhance security by complicating the replication of samples from genuine users. To address this, we present a bimodal deep learning network (BDLN or BNet) that integrates facial and voice modalities. Voice features are extracted using the SincNet architecture, and facial image features are obtained from convolutional layers. Proposed network fuses these feature vectors using either averaging or concatenation methods. A dense connected layer then processes the combined vector to produce a dual-modal vector that encapsulates distinctive user features. This dual-modal vector, processed through a softmax activation function and another dense connected layer, is used for identification. The presented system achieved an identification accuracy of 99% and a low equal error rate (EER) of 0.13% for verification. These results, derived from the VidTimit and BIOMEX-DB datasets, highlight the effectiveness of the proposed bimodal approach in improving biometric security.

Enhancing Cashless Transactions: The Role and Benefits of Fingerprint Technology

In the modern times cashless transactions have become increasingly prevalent in today's digital economy and strong security measures are important to safeguard all the sensitive data related to finances. Fingerprint recognition emerges as a safe solution to upgrade the security and convenience of existing cashless systems. This research paper explores the utilization and benefits of fingerprint recognition./ The paper begins by understanding the basics of this technology, its advancement and uses. It then dives into the integration of cashless transactions with fingerprint recognition systems highlighting the potential benefits. Moreover, this paper also analyzes real- word scenarios and implementation across various industries and geographies. Furthermore, the technical considerations and challenges associated with implementation are also discussed which includes scalability, accuracy, accessibility of the technology. The strategies to make this technology more efficient and compliant are also discussed. In conclusion, this research accentuates the potential to revolutionize the landscape of cashless transactions using fingerprint recognition technology. By addressing and solving the challenges the full potential of biometric technology to upgrade digital payments can be unlocked.

Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.

Forensic Examinations in Cases of Mistaken Biometric Identification

In connection with innovation of modern biometric technologies in various spheres of life, both at the levels of individuals and of the State, there exists an increasing risk of serious negative consequences of potentially possible mistaken identifications. Such cases already happened both in Russia and abroad. In the course of legal proceedings on facts of mistaken identity and its consequences, forensic examinations are carried out in most cases. Depending on the type of biometric registration and identification system, forensic computer-technical, traceological, portrait, video technical examinations as well as forensic medical examination and other medicolegal examinations are assigned. The article gives an assessment of legality and scientific validity of performing forensic anthropometric examinations of photographs and video recordings of the suspects captured on them.

Comparative analysis of a novel spectral-domain OCT biometer versus swept-source OCT or OLCR biometer in healthy pediatric ocular biometry

With the advancements in ocular biometric technology, there have been significant improvements in accurately and efficiently measuring ocular parameters. The aim of this study is to compare the reliability of biometric parameters obtained using a new frequency-domain optical coherence tomography (SD-OCT) biometer with the measurements obtained from swept-source OCT (SS-OCT) and optical low coherence reflectometry (OLCR) biometers. This study employed an observational cross-sectional design. Measurements of axial length (AL), flat and steep corneal keratometry (K1 and K2), and central corneal thickness (CCT) obtained using the The Colombo IOL were compared with those obtained with the IOLMaster 700 and SW-9000 devices. The agreement were evaluated using intraclass correlation coefficient (ICC) and Bland–Altman analyses. The differences of the measurements of the three increments were assessed by one-way ANONA. 73 right eyes of 73 healthy pediatric subjects were analyzed. The AL difference measured by Colombo IOL compared with IOLmaster700 and SW-9000 were 0.00 ± 0.02 mm and − 0.07 ± 0.05 mm, respectively (P > 0.05, ANOVA). There was no statistically significant difference in CCT, K1, and K2 among the three instruments (all P > 0.05, ANOVA). The ICC values for AL, K1, K2, and CCT were 0.999, 0.996, 0.995, and 0.998, respectively. The Bland-Altman analysis showed an agreement of AL, K1, K2, and CCT with Colombo IOL and IOLMaster 700 spanned over 0.08 mm, 0.71D, 0.69D, and 12.17 μm, respectively. The agreement of AL, K1, K2, and CCT with Colombo IOL and SW-9000 spanned approximate 0.21 mm, 0.75D, 1.06D, and 14.37 μm, respectively. The new SD-OCT biometer and the SS-OCT biometer showed strong agreement in measuring AL and CCT in healthy pediatric subjects. This supports the reliability of the new SD-OCT biometer as an alternative for assessing these parameters. However, K1 and K2 could not be used interchangeably in clinical practice. Further research is needed to explore their applicability in different clinical settings and patient populations."light" />

Noninvasive biometric monitoring technologies for patients with heart failure.

Heart failure remains one of the leading causes of mortality and hospitalizations in the US that not only impacts quality of life but also poses a significant public health burden. The majority of affected patients are admitted with signs and symptoms of congestion. Despite the initial enthusiasm, traditional remote monitoring strategies focusing primarily on weight gain failed to improve clinical outcomes. Implantable pulmonary artery pressure sensors provide earlier and actionable data, but most patients would favor forgoing an invasive procedure in favor of an alternative, non-invasive monitoring platform. Several devices utilizing different combinations of multiparameter monitoring to reliably detect congestion have recently been developed and are undergoing testing in the clinical setting. Combining these sensors with the power of artificial intelligence and machine learning has the potential to revolutionize remote patient monitoring and early congestion detection and to facilitate timely interventions by the care team to prevent hospitalization. This manuscript provides an objective review of novel, noninvasive, multiparameter remote monitoring platforms that may be tailored to individual heart failure phenotypes, aiming to improve quality of life and survival.

How Smart Airport is Paving the way for a Smart Access Concept: Technology’s Role in the Digital Ecosystem of the Access Control Process

Airports have undergone a significant digital evolution over the past decades, enhancing efficiency, effectiveness, and user-friendliness through various technological advancements. Initially, airports deployed basic IT solutions as support tools, but with the increasing integration of digital systems, understanding the detailed digital ecosystem behind airports has become crucial. This research aims to classify technological maturity in airports, using the access control process as an example to demonstrate the benefits of the proposed taxonomy. The study highlights the current digital ecosystem and its future trends and challenges, emphasizing the importance of distinguishing between different levels of technological maturity. The role of biometric technology in security access control is examined, highlighting the importance of proper identification and classification. Future research could explore data collection, privacy, and cybersecurity impacts, particularly regarding biometric technologies in Smart Access Level 4.0. The transition from Smart Access Level 3.0 to 4.0 involves process automation and the introduction of AI, offering opportunities to increase efficiency and improve detection capabilities through advanced data analytics. The study underscores the need for global legislative frameworks to regulate and support these technological advancements.

Enhanced facial expression recognition using transfer learning and M-CLAHE

In recent years, Facial Expression Recognition (FER) techniques have gained substantial attention within the realm of biometric technology due to their wide range of applications, including emotion analysis, human-computer interaction, and surveillance systems. This paper presents a robust and efficient FER system composed of three key steps. First, precise face detection is performed using the Viola-Jones algorithm, a well-established method for detecting facial features in real-time. Second, the detected images are enhanced using a Modified Contrast Limited Adaptive Histogram Equalization (M-CLAHE) technique to improve contrast and visibility. Finally, feature extraction and classification are carried out using three powerful Convolutional Neural Network (CNN) architectures: VGG16, ResNet50, and Inception-v3, all benefiting from Transfer Learning to boost performance. Experiments were conducted on two widely-used datasets, JAFEE and CK+, with Inception-v3 achieving remarkable accuracy, reaching 98.43% and 99.93% on the respective databases. These results underline the effectiveness and robustness of our approach, particularly through the use of Transfer Learning, which significantly enhances the overall performance of the model compared to existing methods.

Beyond Empathy: Unveiling the Co-Creation Process of Emotions through a Wearable Device

Emotions fluctuate during the process of social interaction. Although the co-creation of emotions through organizational behavior has been discussed theoretically in existing research, there is no method to demonstrate how emotions are co-created. Instead, previous studies have paid much attention to empathy, in which a person’s emotions are contagious. In contrast to self-report, which is a traditional method that can only assess emotions at a single point in time and adapts to empathy, biometric technology has made it possible to analyze emotional fluctuations over time. However, previous studies have focused only on understanding the emotional fluctuations of individuals separately. In the present study, we developed a system to measure the co-creation of emotions using a wearable device. The pulse rate was converted into valence as a positive–negative emotion, and the fluctuations in valence were analyzed by cross-correlation. We demonstrated the feasibility of the proposed system through triangulation by integrating biometrics with observation and self-report. The proposed system was verified to measure the co-creation of pair and group emotions using real-world data beyond laboratory settings. The present study contributes to business administration by proposing a critical concept for measuring the co-creation of emotions based on a constructionist approach.