Biometric Technology Research Articles

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.

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.

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.

Face Recognition-Based Door Lock Security System Using TensorFlow Lite

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies

A lightweight finger multimodal recognition model based on detail optimization and perceptual compensation embedding

Multimodal biometric recognition technology has attracted the attention of many scholars due to its higher security and stability than single-modal recognition, but its additional parameter quantity and computational cost have brought challenges to the lightweight deployment of the model. In order to meet the needs of a wider range of application scenarios, this paper proposes a lightweight model DPNet using fingerprint and finger vein images for multimodal recognition, which adopts a double-branch lightweight feature extraction structure combining detail optimization and perception compensation. Among them, the detail extraction optimization branch uses multi-scale dimensionality reduction filtering to obtain low-redundant detail information, and combines the depth extension operation to enhance the generalization ability of detail features. The perception compensation branch expands and compensates the model's perceptual field of view through lightweight spatial location query and global information attention. In addition, this paper designs a perceptual feature embedding method to embed perceptual compensation information in the way of importance adjustment to improve the consistency of embedded features. The ABFM fusion module is proposed to carry out multi-level lightweight and deep interactive fusion of the extracted finger modal features from the global to the spatial region, so as to improve the degree and utilization rate of feature fusion. In this paper, the model recognition performance and lightweight advantages are verified on three multimodal datasets. Experimental results show that the proposed model achieves the most advanced lightweight effect and recognition performance in the experimental comparison of all datasets.

Perancangan Sistem Keamanan Rumah Menggunakan Face Recognition (Pengenalan Wajah) Berbasis Raspberry Pi

Talking about technology, currently computer technology is developing very rapidly and is a field that has a very important role in several aspects of human life, including in the field of security. Currently, many home or room security systems have been developed with several identity verification methods using a computer system, using keys, cards, passwords, and so on. However, this method still has shortcomings such as humans' limitations in remembering number combinations which makes the door inaccessible. Therefore, techniques for reliable and accurate identification or verification can be designed using biometric technology that utilizes the special characteristics of the human individual.

Enhancing cell phone security through finger vein biometric authentication systems

The growing demand for secure mobile devices has led to the widespread adoption of biometric technologies. While personal identification numbers (PINs), passwords, gridlock patterns to safeguard phones and facial recognition have become common, they have certain limitations in terms of security and user convenience. These conventional security measures are riddled with susceptibilities, such as password pilferage, memory lapses in authentication details, and user mistakes in grid pattern creation. Due to the rising occurrence of cell phone incursions and theft, there is an urgent requirement for a strong security system that not only defends data but also shields the device itself. This paper proposes a resilient finger vein biometric security system as a more effective substitute for knowledge-based and password-based authentication techniques. The implementation of finger vein biometrics as a novel approach to enhancing cell phone security. The method employs infrared (IR) light transmission to capture the vein patterns and shadows resulting from the different thicknesses of finger muscles, bones, and tissues. The unique vascular patterns in fingers are difficult to forge, offering a higher level of security compared to other biometric systems. The simulations we conducted indicate an identification accuracy rate of 93.82%, indicating that this technique provides a substantial enhancement in preventing unauthorized access and theft of cell phones. This study examines the technology behind finger vein biometrics, its integration into mobile devices, and the security advantages it offers.

Border surveillance: biometric technologies and personal data in Latin American migration

Border surveillance: biometric technologies and personal data in Latin American migration

Air-Writing Recognition Enabled by a Flexible Dual-Network Hydrogel-Based Sensor and Machine Learning.

Accurate air-writing recognition is pivotal for advancing state-of-the-art text recognizers, encryption tools, and biometric technologies. However, most existing air-writing recognition systems rely on image-based sensors to track hand and finger motion trajectories. Additionally, users' writing is often guided by delimiters and imaginary axes which restrict natural writing movements. Consequently, recognition accuracy falls short of optimal levels, hindering performance and usability for practical applications. Herein, we have developed an approach utilizing a one-dimensional convolutional neural network (1D-CNN) algorithm coupled with an ionic conductive flexible strain sensor based on a sodium chloride/sodium alginate/polyacrylamide (NaCl/SA/PAM) dual-network hydrogel for intelligent and accurate air-writing recognition. Taking advantage of the excellent characteristics of the hydrogel sensor, such as high stretchability, good tensile strength, high conductivity, strong adhesion, and high strain sensitivity, alongside the enhanced analytical ability of the 1D-CNN machine learning (ML) algorithm, we achieved a recognition accuracy of ∼96.3% for in-air handwritten characters of the English alphabets. Furthermore, comparative analysis against state-of-the-art methods, such as the widely used residual neural network (ResNet) algorithm, demonstrates the competitive performance of our integrated air-writing recognition system. The developed air-writing recognition system shows significant potential in advancing innovative systems for air-writing recognition and paving the way for exciting developments in human-machine interface (HMI) applications.

Review of farmer-centered AI systems technologies in livestock operations

Abstract The assessment of livestock welfare aids in keeping an eye on the health, physiology, and environment of the animals in order to prevent deterioration, detect injuries, stress, and sustain productivity. Because it puts more consumer pressure on farming industries to change how animals are treated to make them more humane, it has also grown to be a significant marketing tactic. Common visual welfare procedures followed by experts and vets could be expensive, subjective, and need specialized staff. Recent developments in artificial intelligence (AI) integrated with farmers’ expertise have aided in the creation of novel and cutting-edge livestock biometrics technologies that extract important physiological data linked to animal welfare. A thorough examination of physiological, behavioral, and health variables highlights AI's ability to provide accurate, rapid, and impartial assessments. Farmer-focused strategy: an emphasis on the crucial role that farmers play in the skillful adoption and prudent application of AI and sensor technologies, as well as conversations about developing logical, practical, and affordable solutions that are specific to the needs of farmers.

EXPLORING THE ETHICAL CONSIDERATIONS OF BIOMETRICS IN CYBERSECURITY

Abstract—This article offers a thorough investigation of the ethical concerns related to employing biometrics in cybersecurity. It investigates the importance of biometric technology, its effects on privacy and security, and the moral challenges it raises. By extensively examining literature, case studies, and ethical principles, this paper seeks to offer perspectives on how people, institutions, and policymakers can address the ethical complexities associated with using biometrics in cybersecurity while guaranteeing responsible and ethically sound practices. Keywords—Biometrics, Cybersecurity, Privacy, Ethics, Security, Spoofing, Vulnerability, Surveillance

Sparse representation of finger knuckle print images for personal identification

SummaryFraud keeps increasing in our society and security applications become crucial and needed in our daily life. Biometric technology attempts to stop fraud and falsification in different opportunities such as bank services, access to controlled areas or crossing frontiers, by recognizing the identity of a person using his physiological (fingerprint, iris, face) or behavioral modalities (gait, signature). In this article, we focus on an emerging biometric modality called the finger knuckle print (FKP). In fact, this modality has several advantages such as the easy distinction between different persons, stability over time and user acceptance. So, an FKP identification approach is proposed using scale invariant feature transform descriptors based sparse representation method. The classification step, between training and testing FKP samples, is made using the support vector machines method. Experiments applied on two public FKP databases: The Hong Kong Polytechnic University (PolyU) Contactless Finger Knuckle Images Database and the Indian Institute of Technology Delhi (IITD) Finger Knuckle Database, containing respectively 2500 and 790 images, demonstrate high correct identification rates by reaching 98.58% and 99.15% for these two databases.