Biometric Fingerprint Research Articles

Clinic transfers and engagement in HIV care during the perinatal period across a network of healthcare centers in Lilongwe, Malawi

IntroductionDisengagement from HIV care during the perinatal period remains a challenge. Improving engagement in HIV care requires monitoring engagement across multiple indicators, including retention in HIV care, visit adherence, clinic transfers, and viral suppression to support improved clinical and programmatic outcomes.MethodsWe enrolled a prospective cohort of pregnant WHIV across a network of five urban clinics in Lilongwe, Malawi from February 2020-February 2021. WHIV were followed from their first antenatal care visit through 9 months postpartum across all study sites using biometric fingerprint scanning. Study visits occurred at enrollment into antenatal care, 6 weeks’, 6 months, and 9 months postpartum. In addition, all usual care HIV visits were captured via medical records. Participants who missed a study visit or usual care visit were traced. We evaluated determinants of multiple indicators of engagement in care, including retention in HIV care (attending a scheduled visit or self-reported recent visit when traced), HIV visit adherence (missed scheduled HIV visits and HIV visit coverage), clinic transfers, and viral load suppression (< 1000 copies/mL) using modified Poisson regression and sub-distributional hazard ratios to account for competing events of death and loss-to-follow-up. Associations between clinic transfer and subsequent indicators of engagement in HIV care were evaluated using generalized estimating equations.ResultsAmong 399 participants, 81% were on ART at baseline. Retention in HIV care was 87% at 6 weeks postpartum, 77% at 6 months postpartum and 89% at 9 months postpartum. At 9 months postpartum, 91% of participants were virally suppressed, 81% had missed a scheduled HIV visit, and 19% had transferred clinics. WHIV who transferred clinics were most likely to miss their subsequent scheduled HIV visit by ≥ 30 days. Transferring clinics was not associated with unsuppressed viral load or non-retention at 9 months postpartum.ConclusionsIn a cohort of WHIV, retention and viral load suppression were high in the perinatal period, but missed HIV visits and clinic transfers were common. Transferring clinics was associated with an increased likelihood of missing a subsequent HIV visit. Clinic transfers may be important indicators of disruptions in clinical care for WHIV in the perinatal period.

Utilization of Internet of Things (IoT) for Biometrics Attendance and Web-Based Student Monitoring

SMPN 31 Tangerang aims to enhance the accuracy and efficiency of its student attendance system. The current problem is that the existing attendance system is easy to manipulate and lacks accuracy. This research aims to develop a biometric attendance system based on fingerprint technology to address these issues, with output presented in a web page format and without using paper. The methods used in this study include literature review, interviews, and observations for data collection. Development methods involve system design and the waterfall model. The developed attendance system utilizes IoT (Internet of Things) technology and electronic circuits to facilitate implementation and use. The research results show that the biometric fingerprint attendance system can improve student discipline. The system allows for attendance monitoring through a web interface, with data that can be backed up to Microsoft Excel. This device offers high accuracy, good attendance speed, and ease of use for students. In conclusion, the implementation of the biometric attendance system and web-based monitoring meets the school's needs for efficient and effective attendance management and monitoring.

Contribution of Biometric Fingerprint in Reducing Prevalence of Unauthorized Absenteeism among Employees in Public Organizations in Moshi Municipality

This study investigated the contribution of biometric fingerprint technology in reducing unauthorized absences among employees in public organizations within Moshi Municipality, Tanzania. Grounded in the Social Control Theory, the research utilized a convergent parallel mixed-methods approach. The target population consisted of 3,218 public employees from 45 organizations, with a sample of 356 respondents. The study collected qualitative and quantitative data, ensuring validity and reliability through content, face validity, and Cronbach's alpha. Quantitative data was analyzed using descriptive statistics in SPSS, while qualitative data underwent context analysis. Ethical guidelines were strictly adhered to. The findings revealed that public servants in Moshi Municipality perceived biometric fingerprint technology as a reliable solution for addressing unauthorized absences in the workplace. Implementation of this biometric has created a secure and accountable work environment. The study concludes that the use of biometric fingerprint technology has positive contributions to attendance management, accountability, and productivity of public organization employees. Further, it was recommended that public organizations prioritize clear communication with employees about the purpose, benefits, and safeguards of biometric fingerprint technology. Additionally, further research is suggested to explore the challenges faced by employees in utilizing this technology, with the aim of contributing to the existing knowledge base on biometric fingerprints in public organizations.

Multimodal Biometric System Fusion Using Fingerprint and Iris with Convolutional Neural Network

Biometric sensing technology became everyday life frequent component as a result of world requirement for info security and safety legislation. A strong and efficient individual authentication has appeared because of new developments in multimodal biometrics. Multimodal biometrics integrates different biological traits in trying for creating considerable effect on identification performance. Latent fingerprint biometrics refer to effective human identification system for criminals given the accessible crime evidence shreds. Although, biometric trait restrictions like intra-class variation, sensed data noise, lack of individuality caused low matching score that possesses a negative effect on recognition and investigation process. This paper uses two unimodal biometrics—the fingerprint and the iris—applied as multi-biometrics to show that using these biometrics can produce excellent results with high accuracy. Every biometric result is weighted for involvement in the final decision, and the decision level is utilized for fusion. For every biometric result integration effect, a neural network is used. The datasets' experimental findings have demonstrated a notable biometric system identification capability. The accuracy performance of the suggested approach is 100, the FAR is 0.1, and the EER is 0.1. To demonstrate the efficacy of the suggested system, the suggested method is contrasted with a few other approaches currently in use.

Design of Robotics Implementation of A Fingerprint Biometric System

Biometrics, rooted in the Greek terms "bios" (life) and "metrikos" (measure), represents a modern approach to identifying individuals based on their unique physical characteristics, such as fingerprints, facial features, and iris patterns. In today's increasingly digital landscape, traditional security methods like passwords and keys have become inadequate in safeguarding sensitive information. The rise of sophisticated cyberattacks and unauthorized access has highlighted the need for more advanced and secure authentication methods. This project aims to design and implement a fingerprint biometric system using robotics and FPGA technology. The system is intended to provide a more reliable and secure method of verifying identities by capturing and processing fingerprint images. It will extract minutiae points, the unique features within a fingerprint, to create a robust authentication mechanism. This approach promises to enhance security by offering an alternative to conventional methods that are often vulnerable to breaches. In developing this biometric system, the focus will be on ensuring that it can be effectively integrated into various applications where secure access control is crucial. By leveraging the unique and immutable nature of fingerprints, the system will reduce the risks associated with password reuse, theft, and unauthorized sharing. The inclusion of continuous authentication techniques will further strengthen security, ensuring that the user’s identity is verified throughout a session, not just at the initial login. Additionally, the project will incorporate version control mechanisms to track the development and deployment of the system, ensuring that any changes or updates can be easily managed and reviewed. This will help maintain the system's reliability and effectiveness over time, allowing it to adapt to evolving security challenges. Through this work, we aim to contribute to the broader adoption of biometric technologies in securing digital and physical environments, paving the way for more secure and seamless user experiences.

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

Pattern of Bacterial Flora and Effect of Disinfection on Fingerprinting Biometric Devices at a Tertiary Care Health Facility: An Interventional Study.

Introduction Recently, many public and private sector institutions and hospitals have installed biometric fingerprint devices for attendance purposes. This step is taking us toward modernization but biometric devices have their cons and pros; if not sterilized at regular intervals, then it may be a potent cause of transmission of various infections. Many studies have reported the presence of coagulase-negative Staphylococcus aureus (CONS), methicillin-resistant Staphylococcus aureus (MRSA), Escherichiacoli, Pseudomonas, and others. Aim To study the pattern of bacterial flora and the effect of disinfection on fingerprinting biometric devices at a tertiary care health facility. Materials andmethods A total of 138 biometric devices were used, out of which 105 were frequently (at least 50 uses per day) used and functional. So, 105 samples were collected on day zero (baseline), of which 43 and 62 were from clinical and non-clinical groups, respectively. The devices were disinfected with isopropyl alcohol (w/v 70%) and subsequent samples were taken on day 1 (after 24 hours) and day 7. The samples were collected and transported to the microbiology lab for culture and incubation. Statistical analysis was performed usingSPSS software version 25 (IBM Corp., Armonk, NY) employing chi-square, Cochran's Q test, and post hoc test. A p-value ≤0.05 at a 95% confidence interval was considered to be statistically significant. Results At baseline (day 0), bacterial growth was observed in 13 (38%) devices from the clinical group and 10 (20%) from the non-clinical group. After disinfection with 70% isopropyl alcohol, bacterial growth was reduced by 83% on day 1 but increased by 82% on day 7. These changes were statistically significant (p ≤ 0.05). Conclusion The present study concluded the definite presence of bacterial flora on the biometric fingerprint devices which are prone to carry and transmit microorganisms indirectly from person to person. The surface of biometric fingerprinting devices should be disinfected daily. If not possible, it should be done on an average of every third day to control and minimize the transmission of microorganisms.

BIOMETRIC ATTENDANCE SYSTEM

This paper presents a biometric fingerprint attendance system designed to enhance accuracy, security, and efficiency in attendance tracking. The system utilizes fingerprint recognition technology for data acquisition, preprocessing, feature extraction, and matching. Key challenges, including data privacy and security, are addressed with robust solutions. Comprehensive testing demonstrates the system's effectiveness in reducing time theft and improving employee accountability. The findings highlight the potential of biometric systems to revolutionize attendance management, suggesting avenues for future technological advancements. biometric fingerprint attendance system aimed at improving accuracy and security in attendance tracking. Utilizing fingerprint recognition technology, the system effectively handles data acquisition, processing, and matching. Key issues such as data privacy and security are addressed with robust solutions. Testing shows significant improvements in reducing time theft and enhancing employee accountability, highlighting the system's potential to revolutionize attendance management.

Vehicle Theft Authentication System

This thesis presents a robust security system designed to tackle the escalating issue of vehicle theft and unlicensed driving. By integrating a suite of cutting-edge technologies including Arduino, driver's permit card (DL),radio-frequency ID scanner (RFID),biometric fingerprint sensor (FP), Face Recognition (FR), and Global System for Mobile Communication modem (GSM), the system aims to fortify vehicles against unauthorized access. At its core, Arduino serves as the central intelligence, capable of storing and processing authorized driver data such as facial recognition and fingerprint records. When a driver inserts their license into the RFID reader, the system cross-references the information with stored data. If the license is validated, the system proceeds to authenticate the driver's identity through facial recognition and fingerprint scanning. Successful verification enables the ignition system, granting access to the vehicle. However, any discrepancy triggers an immediate SMS alert via the GSM modem to the vehicle owner, while simultaneously disabling the ignition to thwart unauthorized use. Additionally, an alcohol detection module further enhances safety by preventing vehicle ignition if alcohol is detected in the driver's system. This comprehensive approach not only safeguards vehicles from theft but also promotes responsible driving practices, exemplified by timely license renewal reminders and the enforcement of alcohol-free driving. Key Words: Vehicle security system,Arduino integration,Driver's license card (DL), radio-frequency ID scanner (RFID),Fingerprint module (FP) Face Recognition (FR), GSM-enabled mobile communication device, Unauthorized access prevention,Facial recognition,Fingerprint scanning SMS alerts,Alcohol detection module,Responsible driving practices,License renewal reminders,Alcohol-free driving enforcement.

The Self-Detection Method of the Puppet Attack in Biometric Fingerprinting

The Self-Detection Method of the Puppet Attack in Biometric Fingerprinting

Toward Synthetic Physical Fingerprint Targets.

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

High uptake of menstrual health information, products and analgesics within an integrated sexual reproductive health service for young people in Zimbabwe

BackgroundDespite being integral to women’s well-being, achieving good menstrual health (MH) remains a challenge. This study examined MH services uptake (including information, analgesics, and a choice of MH products - the menstrual cup and reusable pads) and sustained use of MH products within an integrated sexual and reproductive health intervention for young people in Zimbabwe.MethodsThis mixed-methods study was nested within a cluster randomised trial of integrated sexual and reproductive health services (CHIEDZA) for youth in three provinces (Harare, Mashonaland East, and Bulawayo). The study collected qualitative and quantitative data from 27,725 female clients aged 16–24 years, who accessed CHIEDZA from April 2019 – March 2022. Using a biometric (fingerprint recognition) identification system, known as SIMPRINTS, uptake of MH information, products, and analgesics and other services was tracked for each client. Descriptive statistics and logistic regression were used to investigate MH service uptake and product choice and use over time, and the factors associated with these outcomes. Thematic analysis of focus group discussions and interviews were used to further explore providers’ and participants’ experiences of the MH service and CHIEDZA intervention.ResultsOverall, 36,991 clients accessed CHIEDZA of whom 27,725 (75%) were female. Almost all (n = 26,448; 95.4%) took up the MH service at least once: 25433 took up an MH product with the majority (23,346; 92.8%) choosing reusable pads. The uptake of cups varied across province with Bulawayo province having the highest uptake (13.4%). Clients aged 20–24 years old were more likely to choose cups than reusable pads compared with those aged 16–19 years (9.4% vs 6.0%; p < 0.001). Over the implementation period, 300/1819 (16.5%) of clients swapped from the menstrual cup to reusable pads and 83/23346 (0.4%) swapped from reusable pads to the menstrual cup. Provision of the MH service encouraged uptake of other important SRH services. Qualitative findings highlighted the provision of free integrated SRH and MH services that included a choice of MH products and analgesics in a youth-friendly environment were key to high uptake and overall female engagement with SRH services.ConclusionsHigh uptake demonstrates how the MH service provided much needed access to MH products and information. Integration of MH within an SRH intervention proved central to young women accessing other SRH services.

Wired Fingerprint-Based Classroom Attendance System for Secured Student Attendance Archiving Using Arduino UNO Microcontroller

This study, which successfully addresses the shortcomings of traditional attendance-checking methods, such as human error, that are inevitable in manual attendance systems given the fact that it is time-consuming. Paper-based systems can be susceptible to forgery, as students may attempt to sign in on behalf of absent classmates. This undermines the integrity of attendance records. Introduces a fingerprint-based classroom attendance system designed using the Arduino Uno microcontroller. The research explores the feasibility of fingerprint biometrics for identity verification in educational settings. Using Arduino Uno, Fingerprint Sensor, RTC Module, and the LCD Monitor the researchers successfully developed a working prototype for the Wired Fingerprint-Based Classroom Attendance. 600 tests were applied to collect the (1.0) lowest and (2.0) highest time of the fingerprint sensor and calculate its average (1.7). The developed system operates offline, storing data securely on an SD card, making it particularly suitable for institutions in areas with restricted internet access. Comparative performance evaluations against conventional pen-and-paper methods highlight the fingerprint-based system's notable capacity, accuracy, positioning it as a transformative tool to enhance attendance tracking procedures and eliminates attendance-related issues to improve overall classroom operations.

Biometry based Exam Door Verification System with SMS Alert

In the dominion of educational institutions, the security and integrity of examinations are of paramount importance. Traditional methods of verifying student identities using photo ID cards and signatures have proven inadequate in the face of evolving challenges related to impersonation, cheating, and unauthorized access to examination halls. To address these concerns, this project introduces a novel Biometry-Based Exam Door Verification System with SMS Alert, employing STM32 microcontroller technology for advanced security and real-time notifications. The system leverages biometric data, specifically fingerprint recognition, to ensure swift and accurate student identity verification. By implementing the STM32 microcontroller as the system's core, it manages data processing, biometric authentication, and communication functions seamlessly. The inclusion of an SMS alert system enables instant notifications to exam administrators, facilitating immediate responses to student check-ins and potential security breaches. The project reflects a forward-thinking approach to examination security, embracing technological advancements to address contemporary challenges in educational institutions. It offers an innovative and scalable solution for institutions seeking to uphold the integrity of examinations while improving overall administrative efficiency. Key Words: Biometric-Based Exam Door Authentication System, STM32 microcontroller technology, advanced security, real-time notifications, biometric data, biometric fingerprint module, etc.

Empowering Manets with Advanced Multimodal Biometric Authentication and Encryption

In a mobile ad hoc network (MANET), nodes communicate wirelessly, facing unique challenges. Traditional MANETs suffer from issues like erroneous transmission and vulnerability to unauthorized nodes joining the network, posing security risks. Authentication within MANETs is a significant security concern, prompting ongoing research for enhancements. Our solution integrates multimodal biometric authentication with RSA and AES encryption, providing robust security for user authentication and data protection in MANETs. This approach effectively addresses risks such as unauthorized access and data tampering, crucial for secure communication in dynamic, resource-limited MANET environments. Our proposed system utilizes a combination of face and fingerprint biometrics for encryption, enhancing network security. Through testing, our system demonstrates a high authentication rate of 92.42% with minimal processing times: 0.042 ms for key generation, 0.019 ms for encryption, and 0.032 ms for decryption, based on a 1024-bit key size. These practical results showcase the resilience and efficiency of our secure system.

Dynamical Analysis of a Quadratic Megastable Chaotic Oscillator and Its Application in Biometric Fingerprint Image Encryption

This investigation centers on megastable systems, distinguished by their countable infinite attractors, with a particular emphasis on the Quadratic Megastable Oscillator (QMO). Unlike traditional megastable oscillators reliant on external excitation, our proposed QMO operates autonomously, contributing to its distinctiveness. Through a comprehensive exploration of the QMO, we elucidate various dynamical behaviors, enriching the understanding of its complex system dynamics. In contrast to conventional megastable oscillators, the QMO yields nested types of multiple attractors for diverse initial conditions, elegantly depicted in phase portraits. To gauge the sustainability of chaotic oscillation, we employ influential parameter bifurcation plots, providing a nuanced insight into the system’s dynamical evolution. The complexity of the proposed system is further underscored by its intricate basins of attraction, accommodating an infinite number of coexisting attractors. Exploring trajectory dynamics, we observe that certain initial conditions lead trajectories to distant destinations, evading the influence of local attractors. This behavior underscores the uniqueness of the QMO and highlights its potential applications in scenarios requiring nonlocalized attractor behaviors. Taking a practical turn, the QMO is applied to biometric fingerprint image encryption, demonstrating its efficacy in real-world applications. Rigorous statistical analyses and vulnerability assessments confirm the success of the QMO in providing secure encryption within chaotic system-based frameworks. This research contributes not only to the theoretical understanding of megastable systems but also establishes the QMO as a valuable tool in encryption applications, emphasizing its robustness and versatility in complex dynamical scenarios.

Rubik's Encryption Combined with CNN for Biometric Authentication

In response to the rapid evolution of technology, safeguarding user data has become paramount, necessitating a robust solution against diverse cyber threats. This research project proposes a comprehensive approach that integrates biometric authentication, specifically iris and fingerprint recognition, with a sophisticated encryption system based on Rubik's Cube and facilitated by Convolutional Neural Networks (CNNs). Biometric authentication establishes an unassailable link between data records and individuals, ensuring heightened accuracy and security. To counter potential threats, the project incorporates a resilient technique known as zero-bit watermarking, embedding minimal information without compromising the original biometric data's size or quality. The integration of Rubik's Cube-based encryption adds an additional layer of complexity to data protection, employing a dynamic and visually intricate encryption algorithm. The amalgamation of iris and fingerprint authentication leverages the unique biological characteristics of individuals, creating a multi-layered security approach. The CNNs play a pivotal role as the backbone for efficient feature extraction and authentication decision-making, enhancing the overall system's accuracy and reliability. This innovative fusion of cryptographic methods and biometric authentication offers a comprehensive solution for safeguarding sensitive information across diverse applications, from secure data storage to fortified communication channels, addressing the evolving landscape of cybersecurity challenges.

SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

Unveiling intra-person fingerprint similarity via deep contrastive learning.

Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.

A review of terahertz metamaterial sensors and their applications

Terahertz (THz) waves possess unique characteristics such as high transmission, strong absorption, and low photon energy. THz metamaterial sensors, as label-free and rapid optical sensors, offer significant advantages in detection technology. These sensors enable high-sensitivity, efficient, non-destructive, and low-dose sensing due to the properties of THz waves, the sensitivity of metamaterial devices to changes in the surrounding dielectric environment and the characteristic fingerprint spectrum of biochemical molecules. THz metamaterial sensors hold great potential for applications in biomedicine, non-destructive testing, as well as the food and agricultural sectors. This paper provides a comprehensive review of various detection methods employed by THz metamaterial sensors, which consists of THz refractive index sensors, biometric fingerprint (characteristic absorption peak) sensors and biomass sensors. The study critically discusses different device structures and modulation principles used in these sensors, analyzing their respective strengths weaknesses and development trend. This review represents a broad introduction for beginners.