Biometric Authentication Research Articles

An efficient secure cryptosystem using improved identity based encryption with multimodal biometric authentication and authorization in cloud environments

An efficient secure cryptosystem using improved identity based encryption with multimodal biometric authentication and authorization in cloud environments

Secure System with Security Protocol for Interactions in Healthcare Internet of Things

The Internet of Things is a network of connected devices that can communicate and share data over the Internet. These devices often have sensors that collect data for various purposes, such as usage statistics, data processing, or performing specific actions based on the collected data. Also, medical Internet of Things devices are crucial in monitoring critical functions, measuring blood glucose levels, indicating when patients require medicine, and ensuring timely medication delivery. Communication in the Internet of Things is demanding, requiring diverse protocols that address communication security concerns. These protocols must be robust and secure, considering technical factors such as the network’s objective, energy requirements, and the nature of the communication because they can be exploited. This paper proposes an innovative system with a security protocol that supports and improves communication security in modern Internet of Things networks. The protocol aims to enhance communication safety between interconnected devices for information exchange in medicine or healthcare, ensuring the confidentiality and integrity of sent data and devices. The proposed protocol, tested through formal and automated verification, meets all security goals, including identity verification, anonymity protection, and access revokement. It also protects against Man-in-the-middle, modification, replay, and impersonation attacks.

Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS

This study examines the issues of privacy protection, data security, and query efficiency in blockchain-based electronic medical record (EMR) sharing. It proposes a secure storage and sharing scheme for EMR based on Hyperledger Fabric and the InterPlanetary File System (IPFS). To mitigate the privacy risks of data mining that could reveal patient identities, we establish an attribution channel in Hyperledger Fabric to store EMR ownership information and a data channel to store the storage location, digest, and usage records of medical data. Encrypted medical data are stored in the IPFS. To improve query efficiency in the blockchain, we integrate queryable medical data attributes into a composite key for conditional queries, avoiding complex data filtering processes. Additionally, we use a zero-knowledge proof combined with smart contracts for decentralized identity verification, eliminating reliance on third-party centralized verification services and enhancing system security. We also integrate AES and proxy re-encryption techniques to ensure data security during sharing. This scheme provides a more secure, efficient, and privacy-preserving approach for EMR systems, with significant practical implications and broad application potential.

The Shield of Privacy in the Digital Age: The Clash between Facial Recognition Technology and Personal Information ProtectionCase Analysis and Strategy Discussion

Driven by the wave of digitalization, facial recognition technology has become a key tool for identity verification and security monitoring. However, with its widespread application in daily life, issues of personal privacy security have also become prominent. This study delves into the complexities of facial recognition technology in the realm of personal information security through case analysis, revealing its convenience as an identity verification tool and the risks it poses as a potential means of privacy infringement. The research aims to identify and analyze the ethical and legal issues faced by facial recognition technology and to propose strategies for strengthening personal information protection. This paper integrates literature review, case studies, and international comparative analysis to provide support and recommendations for the practice and policy-making of personal information protection in the digital age.

Priority-based Multi-feature Vector Model Using Convolution Neural Network for Biometric Authentication

Many societal institutions have increased standards for the efficacy and dependability of identification systems in response to the ever-increasing sophistication of computer technology. Biometric identity systems have mostly supplanted the usage of conventional key and smart card systems in highly secure industries. There has been a dramatic increase in the adoption of biometric authentication systems in the last decade. While many biometric systems have relied on exterior physiological traits like fingerprints, iris scans, palm prints, and faces, very few have investigated the possibility of using interior physiological traits as a biometric. It is challenging for the benefits of unimodal biometric technology to be realized in real-world applications because to its inherent constraints, such as the fact that it only proposes a single piece of information and that environmental factors can impact data verification. While passwords have served their purpose of authenticating users and controlling access, they have also revealed their weaknesses. Concerns about forging or impersonating authentication have prompted the proposal of various Artificial Intelligence (AI)-based solutions. A lot of people are interested in user authentication based on ECGs these days. Unlike other biometrics, an electrocardiogram verifies that the person is actual and alive, making it one of the most trustworthy advanced authentication methods. The P, Q, R, S, and T characteristic points of an ECG signal are responsible for its most salient features. Feature selection is a statistical method that can be implemented in a supervised or unsupervised setting via regression or classification. A Convolution Neural Network (CNN) is a type of neural network that extracts and feeds into another neural network, which then classifies those extracted features. The ECG signal serves as input to a feature extraction network. The neural network performs categorization based on the retrieved feature signals. To overcome the problem that the recognition accuracy of traditional ECG identification methods declines with the rise in the number of testing samples at various moments or throughout different heartbeat cycles, this research offers a multi feature reuse model. In this research, an Associated Priority-based Weighted Multi-Feature Vector model using Convolution Neural Network (APbWMFV-CNN) is proposed for ECG signal-based authentication. The proposed model selects the most appropriate features used for accurate biometric model. The comparison results represent that the proposed model performance in user detection is high.

Research Paper on Transformative Innovations in Identity Verification and Recognition

Integrating real-time human detection into identity authentication greatly improves both security and user experience. This strategy decreases fraud risk by analysing physiological and behavioural markers such as facial and eye movements. Its implementation in financial, healthcare, e-commerce, and law enforcement sectors promise to strengthen security measures. Although obstacles exist, the benefits of this human-centered approach are significant, paving the path for a safer digital future. Keywords: Identity verification, biometric authentication, digital security, real-time human detection, fraud prevention, user experience, computer vision, physiological and behavioural clues, facial and vocal patterns, banking, healthcare, e-commerce, law enforcement.

Decentralized Chatting Application Using Blockchain Technology

Abstract: The emergence of blockchain technology has spurred innovation in various domains, including decentralized communication systems. In this context, a decentralized chat application leveraging blockchain, Remix Ethereum IDE, Ganache blockchain, and MetaMask offers a novel approach to secure and privacy-centric messaging. The application harnesses blockchain's immutable ledger to store chat messages, ensuring data integrity and censorship resistance. Remix Ethereum IDE provides developers with a robust environment for crafting smart contracts, the backbone of the chat application's functionality. Developers can design and deploy smart contracts defining chatroom logic, message encryption, and user authentication. Ganache blockchain serves as a local testing environment, enabling developers to validate smart contracts and simulate blockchain interactions in a controlled setting. This facilitates rapid iteration and debugging during the development phase. MetaMask acts as the gateway for users to access the decentralized chat application. Through MetaMask, users authenticate securely using their Ethereum wallets, ensuring pseudonymous identity verification and enabling seamless interaction with the Ethereum blockchain. Together, these technologies form the foundation of a decentralized chat application that prioritizes privacy, security, and user control. By leveraging blockchain and associated tools, the application offers a compelling alternative to centralized messaging platforms, empowering users with sovereignty over their data and communication.

Blockchain Solutions for Authorization and Authentication

Recently, most people have been looking for smart services that allow them to do their daily services directly without having to visit local businesses and providers [1]. As observed in recent times, citizens can now apply to open their own commercial file or obtain a passport without having to visit the country's economic institution or the Immigration and Passports headquarters. Additionally, applying or opening accounts in virtual banks also added a lot of services and facilities for users and transactions between countries that can be rapidly provisioned with minimal effort [2]. All these transactions require the user's authentication and identity verification to avoid any personal fraud and save the user's rightsBlockchain technology stands out as one of the most secure technologies available enabling secure transactions without the need for a central authority. Starting in 2009 1, with Bitcoin leveraging blockchain technology, there has been an increasing number of blockchain technology-based solutions. The significance of this work compared to its predecessors is that it uses an existing product and technology to prove the solutions offered by Blockchain and find a solution for authorization and authentication.

Design and Implementation of a Self-Supervised Algorithm for Vein Structural Patterns Analysis Using Advanced Unsupervised Techniques

Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies.

Identity verification of monoclonal antibodies by triple injection capillary zone electrophoresis.

This paper presents an approach based on triple injection capillary zone electrophoresis for identification of monoclonal antibodies. The analyte to be identified is injected between two zones of a known reference. The distances between the reference zones (plug I and III) and the target zone (plug II) are adjusted by partial electrophoresis of the first and second injection plugs. The full migration time of the target analyte is calculated from the observed migration time by considering the migration times of the reference in the first and third injection plugs. The relative migration time, that is, the ratio between the full migration time of the analyte and the migration time of the reference in the third injection plug provides the basis for identification. Here, eight monoclonal antibodies, including a pair of biosimilars, were used interchangeably as both analyte and reference to investigate potential of the method. The relative migration time for a preliminary positive identification were found to vary between 0.994 and 1.006 (1.000±0.006, p=95%). Beside the relative migration time, isoform distribution, peak profiles, and early migrating peaks, originating from components in the pharmaceutical formulations, were successfully used to verify the identity of all tested monoclonal antibodies.

Novel Approach on Peer-to-Peer Carpooling Using Blockchain

Abstract: Peer-to-peer (P2P) carpooling platforms have emerged as a promising solution to address transportation inefficiencies, reduce traffic congestion, and alleviate environmental impacts. However, existing P2P carpooling systems encounter challenges related to trust, security, and transparency. This work proposes a novel approach to enhance P2P carpooling through the integration of blockchain technology. Blockchain, a decentralized and immutable ledger, offers inherent advantages such as trust, transparency, and security. By leveraging blockchain, this proposed system aims to address key issues in P2P carpooling, including identity verification, payment settlement, and data integrity. Smart contracts, programmable self-executing agreements, enable automated transactions and enforce rules within the carpooling ecosystem.

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.

AI Powered Contactless Fingerprint Recognition

This comprehensive review delves into the realm of AI-powered contactless fingerprint recognition. We trace its evolution from traditional methods to modern contactless approaches, highlighting the pivotal role of AI and machine learning. Key topics include the principles of contactless fingerprint data acquisition, challenges like environmental variations, and privacy concerns. We examine the state-of-the-art AI techniques such as deep learning and convolutional neural networks, showcasing how they enhance accuracy and robustness in contactless fingerprint recognition. The paper also explores diverse applications, spanning security, access control, and healthcare, while addressing societal impacts and ethical considerations. Furthermore, we provide insights into ongoing research and future prospects, including potential improvements, emerging trends, and multi-modal authentication possibilities. This concise review serves as an essential resource for researchers, practitioners, and policymakers interested in the intersection of AI and biometric authentication technologies. Keywords: - AI-powered, Contactless fingerprint recognition, Biometrics, Artificial intelligence, Machine learning, Data acquisition, Deep learning, Convolutional neural networks

Online Signature Biometrics for Mobile Devices.

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries.

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

An Extensive Analysis on Zero Trust Architecture

Zero Trust Architecture (ZTA) addresses a change in perspective in cyber security, challenging the conventional security-based model by expecting no certain trust inside or outside the network limits. This approach exemplifies the standards of constant confirmation, strong access controls, and the idea of "never trust, always verify" (Stafford, 2020). ZTA is intended to address the weaknesses inborn in conventional security models, particularly even with dynamic IT environments, cloud services, and the rising refinement of cyber-attacks. This paper presents a top-tobottom investigation of ZTA, its main components including severe identity verification, least privilege access, micro-segmentation, and multifaceted verification, as well as its fundamental relationship with Identity and Access Management (IAM) solutions. Moreover, this paper looks at the critical job of ZTA in lowering the attack surface, strengthening an organization's security posture, and ensuring regulatory regulations are being followed. This paper’s goal is to examine the shortcomings and weaknesses of conventional perimeter-based security models in the current digital environment and to suggest Zero Trust Architecture (ZTA) as a more potent security paradigm to deal with these issues. This research attempts to give insights into how businesses might switch from traditional security techniques to ZTA to improve their security posture.

Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

YOLO-FaceNet Fusion: Innovative System for Facial Recognition and Feature Extraction

Facial verification is vital in domains like Heathcare, Identity verification, etc. study presents a sophisticated facial verifying system utilizing state-of-the-art technologies: ArcFace is utilized for the edification of the model, while FaceNet is employed for the elicitation of facial characteristics. Initially, YOLO is deployed to discern and extricate visages from the input effigies. Subsequently, FaceNet is utilized to derive high-caliber facial attributes. In the final stage, ArcFace is employed for the model's edification, thus augmenting its robustness and precision. The apparatus gauges facial resemblance employing the Euclidean distance metric and is appraised through measures such as recall, accuracy, and precision. This approach yields a dependable and accurate facial verification system, thereby enhancing operational efficiency in practical applications.. Keywords – You Only Look Once (YOLO), ArcFace, Convolutional Neural Network (CNN), FaceNet, Machine Learning, Deep Learning, Euclidean Distance.

Revolutionizing Identity Verification and Recognition

The innovative integration of real-time human detection improves both security and user experience in identity authentication. This approach improves authentication processes by analysing physiological and behavioural indicators such as face movements and eye movements, hence reducing fraud risks. Its deployment across sectors promises to enhance security in finance, healthcare, e-commerce, and law enforcement. Despite the hurdles, the benefits of this human-centered approach are enormous, indicating a more secure digital future. Keywords— Identity verification, live human detection, digital security, biometric authentication, user experience, fraud prevention, computer vision, physiological and behavioural cues, facial and vocal patterns, finance, healthcare, e-commerce, law enforcement.

Flexible wide-range multidimensional force sensors inspired by bones embedded in muscle

AbstractFlexible sensors have been widely studied for use in motion monitoring, human‒machine interactions (HMIs), personalized medicine, and soft intelligent robots. However, their practical application is limited by their low output performance, narrow measuring range, and unidirectional force detection. Here, to achieve flexibility and high performance simultaneously, we developed a flexible wide-range multidimensional force sensor (FWMFS) similar to bones embedded in muscle structures. The adjustable magnetic field endows the FWMFS with multidimensional perception for detecting forces in different directions. The multilayer stacked coils significantly improved the output from the μV to the mV level while ensuring FWMFS miniaturization. The optimized FWMFS exhibited a high voltage sensitivity of 0.227 mV/N (0.5–8.4 N) and 0.047 mV/N (8.4–60 N) in response to normal forces ranging from 0.5 N to 60 N and could detect lateral forces ranging from 0.2–1.1 N and voltage sensitivities of 1.039 mV/N (0.2–0.5 N) and 0.194 mV/N (0.5–1.1 N). In terms of normal force measurements, the FWMFS can monitor finger pressure and sliding trajectories in response to finger taps, as well as measure plantar pressure for assessing human movement. The plantar pressure signals of five human movements collected by the FWMFS were analyzed using the k-nearest neighbors classification algorithm, which achieved a recognition accuracy of 92%. Additionally, an artificial intelligence biometric authentication system is being developed that classifies and recognizes user passwords. Based on the lateral force measurement ability of the FWMFS, the direction of ball movement can be distinguished, and communication systems such as Morse Code can be expanded. This research has significant potential in intelligent sensing and personalized spatial recognition.