Fingerprint Scanner Research Articles

Development of conductive fingermarks for forensic applications.

Fingermarks are an important form of evidence in forensic science, routinely used for identification or exclusion purposes within the criminal justice system. The increasing use of fingerprint recognition in technology and biosecurity, such as for unlocking devices and accessing banking information, highlights the need for forensic fingermark recovery methods that serve both traditional forensic needs and modern technological demands. Current fingerprint development techniques, however, are not designed to fulfil this dual-purpose. This paper presents a novel approach that introduces the use of conductive paint and silicone to develop, recover, and preserve latent, patent, and plastic fingermarks. The innovative method produces conductive casts that capture detailed ridge patterns, thereby facilitating forensic examination as well as being used for unlocking capacitive and ultrasonic fingerprint scanners in a range of mobile devices.

Tradeoffs between privacy and screening efficiency for air passengers – Implications for tourism supply chain management

ABSTRACT This study employs the stated preference method to explore airline passengers’ choices during security screening and examines their trade-offs between privacy and efficiency. We compare two Asian economic hub cities (i.e. Hong Kong and Shanghai), with 500 business and leisure travellers sampled from each city. A generalised mixed multinomial logit model is estimated. The results suggest that travellers display varied levels of privacy concerns, including biometrics, biographical data, identifiers, and behavioural information. The participants from Shanghai expressed a willingness to go through fingerprint scanning and to provide facial images. The travellers in Hong Kong displayed notable reluctance to receive iris scans and disclose political and religious affiliations, and they were particularly hesitant about sharing medication records. Despite some differences between the two markets, both groups strongly opposed smartphone tracking, including access to search history and GPS location. In general, business (vs. leisure) travellers, men (vs. women), individuals with higher education levels, and those with higher personal monthly incomes strongly preferred fast-track schemes that utilised personal data. Our findings highlight the possibility of making more effective transitions to high-tech screening methods that meet the changing expectations of travellers if total screening time can be significantly decreased.

A Survey on Women Safety and Health Monitoring System

Women safety is a very big issue even in this modern world running with advanced technologies. After a history of times, women got a freedom of equality at the workplace, asset rights, family law, and education. Even Feminist Movement, claimed during 20th Century, Women are not safe anywhere and violence against women and girl child’s happening everywhere during the lonely travelling on roads, picnic spots, work and deserted places. Though we have many armies and super forces to protect everybody, the day to day crime rate against women has not been reduced. There are several safety devices available to protect the women during the violence, but need more human intervention such as entering the data, or shaking the device roughly. We propose a solution for a secure and peaceful environment for women with handbag safety hand-held devices with the aim to provide false proof women safety devices by overcoming the disadvantages in existing system. When we are talking more about Women empowerment, Women achievements it is also very important to think about women safety since a huge numbers (848) of women’s are Indian Women Are Harassed, Raped, Killed Every Day. The proposed work aims at IoT based women safety device by hardware controller attached to the handbag, android application and Bluetooth connectivity in Smartphone. By pressing the controller button, the device alerts the first holder, relatives stored in the database and police when a woman is not safe. The main advantage of the work is, the device works without internet connectivity. Additional features such as protecting the valuable things to be stolen by thieves in crowded places or buses by a separate alarming system, heartbeat sensor setup to monitor heart rate, fingerprint scanner for effecting accessing of the devices and also the mobile android application provide the victim’s location to reach the women and safeguard the women from any harassment at the right time. The main thought of this paper is that it will not only protect the women from physical harassment during odd hours but also safe the women health when it is found abnormal, as a single device comparatively.

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.

Biometrics-Based Generation of Diffie-Hellman Key Exchange Parameters

When two parties need to securely communicate over an insecure channel, Diffie-Hellman is often employed as the key exchange algorithm. This paper presents two novel approaches to generating Diffie-Hellman parameters for key exchange based on user biometrics, namely their fingerprint data. Fingerprint templates are extracted as bit strings via a fingerprint scanner and later used as inputs. In one approach, the whole fingerprint template is utilized as a user’s private key. In the second approach, fingerprint data is scrambled into smaller chunks and rearranged into two strings that serve as the user’s private key and the basis for prime p. Both approaches were implemented and tested experimentally. After analysis, the second approach that uses scrambled fingerprint data shows better execution times and improved security and usability considerations.

Image sensor fusion for multimodal biometric recognition in mobile devices

AimMultimodal biometric recognition authenticates or identifies someone using various biometric features or modalities. Multimodal biometric systems use different biometric qualities to improve the recognition process's accuracy, security, and reliability rather than depending solely on one biometric property, such as fingerprint or facial recognition. Sensor fusion techniques can merge data from several biometric sensors, including fingerprint scanners and facial recognition cameras, to boost the accuracy and dependability of biometric authentication in the context of multimodal biometric recognition in mobile devices. The process of combining data from numerous image sensors to enhance a system's overall performance is known as image sensor fusion. ChallengesChallenges includeintegration difficulties in achieving accurate and secure multimodal biometric recognition on mobile devices. The study goal was Image sensor fusion for multimodal biometric recognition in mobile devices. MethodologyThe study uses a specially made database of 450 face photos roughly 450 fingerprints.Weiner filter (WF) is used for preparing data. Battle Royale optimized with deep convolutional neural network (BRO-DCNN) is proposed to improve the overall performance of fingerprint and facial information. FindingsAccuracy, sensitivity, specificity, and precision are used to measure the effectiveness of the proposed BRO-DCNN system. Study results show that BRO-MLANN solves the many characteristics of smart cities and is an efficient approach compared to other current methods in recognition rate, 98 % accuracy, 99 % precision, 96 % specificity, and 94 % sensitivity.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

Holistic quality assessment and monitoring of YiXinShu capsule based on three-dimensional fingerprints combined with quantitative analysis, antioxidant activity and chemometrics

Ethnopharmacological relevanceYiXinShu capsule (YXSC), originally from the classical TCM formula named “Sheng-Mai-San”, has been extensively utilized in clinic for the treatment of cardiovascular diseases. However, there were few reports about the quality assessment of YXSCs both internationally and domestically. Aim of the studyThe objective was to develop a multi-strategy platform incorporating systematic quantitative fingerprint analysis and antioxidant activity determination, with chemometric analysis and bivariate correlation analysis as the auxiliary approaches, to assess and monitor the quality of YXSCs. Materials and methodsFirstly, according to the Chinese Pharmacopoeia (2020 edition), 12 key indicator components from seven herb medicines were quantified by HPLC method. Then, three-dimensional fingerprints comprising five-wavelength fusion fingerprint (FWF-FP), electrochemical fingerprint (EC-FP) and Differential Scanning Calorimetry fingerprint (DSC-FP) were established to assess and monitor YXSCs using systematically quantified fingerprint method (SQFM) and principal component analysis (PCA). Moreover, by integrating the analysis of the three-dimensional fingerprints, the quality of YXSCs from different batches was effectively screened. Finally, the antioxidant activity of this TCM was assessed through DPPH and ABTS methods, and the L-ascorbic acid equivalent antioxidant capacity (AEAC) values were compared to evaluate the antioxidant activities of the two methods. A Partial Least Squares (PLS) model was used to develop the spectrum-activity relationship between FWF-FP and AEAC, and a bivariate correlation analysis (BCA) was used to assess the correlation between FWF-FP and EC-FP. ResultsThe key indexes including tanshinone I, tol, toe, Atp, first exothermic peak, and second exothermic peak can differentiate between various batches of YXSCs based on their three-dimensional fingerprint profiles. The integration evaluation results from 42 batches of YXSCs were categorized into 2–5 grades, indicating good quality consistency across different batches. In vitro studies have indicated a significant antioxidant activity capacity of YXSCs. The PLS model revealed that 37 out of the 41 fingerprint peaks exhibited antioxidant activity. The overall trend of BCA was consistent with PLS model results. ConclusionThis research presents a scientific and holistic strategy for the quality consistency evaluation of YXSCs, thereby offering an effective approach for the thorough evaluation of TCMs.

Cryptographic Algorithm Designed by Extracting Brainwave Patterns

A new authentication method based on EEG signal is proposed here. Biometric features such as fingerprint scanning, facial recognition, iris scanning, voice recognition, and even brainwave patterns can be used for authentication methods. Brainwave patterns, also known as brain biometrics, can be captured using technologies like electroencephalography (EEG) to authenticate a user based on their unique brain activity. This method is still in the research phase and is not yet commonly used for authentication purposes. Extracting EEG features for authentication typically involves signal processing techniques to analyze the brainwave patterns. Here, a method based on statistics for extracting EEG features is designed to extract meaningful information and patterns from the brainwave data for various applications, including authentication, brain–computer interface systems, and neurofeedback training.

A Study of Dermatoglyphics Patterns in Relation to the Levels of Perceived Stress

Background:: Dermatoglyphics is the study of dermal ridge configuration on the skin of the fingers and palms, which can be used to diagnose chromosomal disorders and individual identification. Stress is characterizedby feeling overwhelmed with mental or emotional pressure and evokes a biological response when any intrinsic or extrinsic stimulus is applied. Stress can cause acute effects and disorders that can trigger structural changes like atrophy and decrement in weight in different brain parts with long-term effects on the nervous system. Materials and Methods:: In this cross-sectional study, fingerprints of 150 participants of Gulf Medical University with an equal number of students and faculty/ staff were obtained by fingerprint scanner. Participants were classified into low, moderate, and high-stress levels by questionnaire. Collected data were statistically analyzed to find any association between dermatoglyphics and perceived stress levels. Results:: Radial Loop was found to have the highest frequency as a percentage in all three groups. Plain whorl was the pattern with the second-highest percentage of occurrences. A notable observation was the lack of plain arch and low frequency of accidental whorl in the high-stress group. Conclusion:: The findings contribute to our understanding of the potential of dermatoglyphics as an indicator of perceived stress, opening up avenues for further research in utilizing these patterns as a non-invasive tool for assessing stress levels.

Biometric System Based Electronic Voting Machine

Through voter identity verification, the avoidance of duplicate voting, and the reduction of problems like ballot rigging and voter impersonation, biometric authentication—more specifically, fingerprint scanning—improves voting security and accuracy. Before enabling voters to cast their ballots, the proposed biometric Electronic Voting Machines (EVMs) use fingerprint sensors combined with microcontrollers such as ARM9. In addition to displaying voter data, such as name, photo, constituency, and ID, following authentication for transparency, these EVMs provide instantaneous transfer of voting results to a central server for oversight. Compared to conventional paper-based or electronic systems, biometric-based EVMs are intended to improve voting process security, efficiency, and integrity.

A Novel Machine Learning-Based Multimodal Biometric Human Verification Using Fingerprint and Face Scan Data

A Novel Machine Learning-Based Multimodal Biometric Human Verification Using Fingerprint and Face Scan Data

Easy and Secure Hostel Hub

The main objective of our project is to secure the hostel hub using fingerprint authentication mechanism technology. In olden days, hostel management system was used for managing all the hostel related transaction such as student admission, food supply, food menu, payment, etc. This will not capture the check in and checkout in our system, this needs either Radio-Frequency Identification (RFID card) or Finger Print scanner. These sorts of devices and its software were cost too much to install and utilize it. In this paper, we are going to record the check in and checkout time of hostel student using fingerprint authentication. Our literature survey, confirmed that finger-print authentication is more secure than RFID card authentication, hencefort we used finger-print authentication in our project. In future depending upon the trends and techniques we will be upgrading the “Easy &amp; Secure Hostel Hub“, system

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.

Adaptive Ambulance Management System

The ability to quickly and accurately retrieve information is essential for emergency medical response. This project integrates biometric technology and web applications to offer a comprehensive solution for improving emergency medical services. Our approach starts with the use of a fingerprint scanner, which records information on patients and guardians and links it to distinctive fingerprints. A Node MCU processes and stores this data, making it possible to quickly and securely identify patients in urgent situations. The Node MCU displays patient information on an LCD panel as soon as they provide their fingerprint. Both ambulance drivers and hospital administration have access to a user-friendly web application simultaneously. The software gives users access to a database of nearby hospitals that is divided into public and private facilities. Soon, the selected hospital will be informed about the patient condition and able to make the treatment arrangements earlier. The patient's preferences and the resources at their disposal can help ambulance crew make wise selections. Ambulance crew have two selection buttons, one is government hospital selection button and another is private hospital selection button. By reducing response times, this function improves patient care. In order to safeguard sensitive patient data, the project places a strong emphasis on data security and privacy. Our goal is to maximize emergency medical response through this integrated system, ensuring that patients receive prompt and efficient care when it counts.

Design and Development of an Automatic Lock System for Cupboards Using Fingerprint Based Microcontrollers

: Seeing the current condition of the community, most of them still use simple security, such as the cupboard security system at Weleri Muhammadiyah 3 Vocational School, the situation is that schools still use one key in one cabinet where only one person is employed while more than one TU officer is if other officers will use the cupboard, they must look for or wait for the key holder first and the method is not effective for emergency matters. The importance of making security in the closet is because the school is the school with the highest number of students and requires safe storage media for diplomas (not yet taken). Therefore, it is necessary to make a cabinet lock application system using microcontroller-based fingerprints. This cabinet lock system uses a fingerprint scanner as the main determinant of whether the cabinet can be opened automatically. From research at Weleri Muhammadiyah 3 Vocational School, this system was able to add a security system to the cabinet to control access rights in the cabinets that were considered important.

Aadhar Based Biometric Electronic Voting System

Flawless voting is guaranteed through the use of Electronic Voting Machines. It is crucial for citizens to have confidence in the security of their votes and to prevent any fraudulent activities. The primary objective of this initiative is to create a secure Electronic Voting Machine that utilizes fingerprint identification technology. To access fingerprints, the AADHAR card database is utilized. During elections, the authentication process for e-voting involves finger vein sensing, which allows voters to cast their votes after electronic ballot reset. Furthermore, the voting data and details of voters can be transmitted to the nearby Database Administration unit via WIFI. The fingerprint scanning feature is implemented to ensure security and prevent issues like fake or repeated voting. This system not only improves accuracy but also enhances the speed of the voting process

Fingerprint Pattern of Fulani and Higgi ethnic groups in Michika LGA, Adamawa State, Nigeria

Developing countries experience surge of insurgency, banditry, cybercrime and other related violence. Criminals often go undetected because of poor forensic capabilities in human detection. Fingerprint features have been very effective in establishment of identity. The aim of this study was to determine fingerprint pattern of Fulani and Higgi ethnic groups of Michika, Adamawa State, Nigeria. A total of 5020 valid fingerprints patterns were captured using live fingerprint scanner. These included; 1230 Fulani male; 1250 Fulani female; 1280 Higgi male; 1260 Higgi female. SPSS version 26.0 statistical software was used for statistical analysis and P &lt; 0.05 was set as a level of significance. Descriptive statistics were employed to determine the frequency of loops whorls and arches in all the fingers of both hands, both sexes and both ethnic groups. Loops were the most frequent pattern in all the observations followed by whorls, and then arches. However, there were relative differences in the percentage proportion across the digits, between left and right, male and female and Fulani and Higgi. The frequency of loop and whorl were equal in right ring (48.8 %) of Fulani male and right thumb (47.7 %) of Higgi male. The frequency of loop was less than that of whorl in left thumb (Loop: 39.8 %; Whorl: 48.8 %) and right thumb (Loop: 43.9 %; Whorl: 48.8 %) of Fulani male. Loops were more frequent in little fingers (85.1 %) followed by middle fingers (73.4 %) and least in thumbs (50.8 %) of left male Higgi; arches more frequent in left hand than right (11.4 % and 7.3 % in Fulani male thumbs respectively). These biological variations might suggest preferential selection of finger as potential predictors of other biological features such as blood group.

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.