Biometric Authentication Research Articles

A Framework for Decentralized Identity and Credential Management Leveraging Blockchain Technology

Abstract: As internet technology rapidly advances and the infrastructure continues to evolve, web applications have become an integral part of our daily lives, with electronic identities (e-identities) playing a crucial role. Today, e-identities are vital for verifying personal identity in numerous online activities, and as our dependence on them grows, so does the concern for their security. This increasing importance of e-identity security raises various challenges, highlighting the need for robust solutions. In response to these challenges, this article introduces a novel solution: a blockchain-based decentralized identity and credential management framework. This approach not only enhances security but also offers greater control and flexibility for users. The article delves into the intricacies of the framework, detailing its design and operational mechanism. Additionally, it explores a range of practical applications, demonstrating the framework's versatility and effectiveness through diverse case studies. This comprehensive examination underscores the framework's potential to revolutionize e-identity management and addresses the pressing need for secure and efficient online identity verification in our increasingly digital world.

Dynamic quantum secret sharing with identity verification based on quantum walks

Abstract A dynamic quantum secret sharing protocol with authentication is proposed based on quantum walks. The protocol can share specific secrets and also update the secret information periodically based on the existing quantum walks structure. Participants can be added or removed while keeping the original secret unchanged. This is more flexible in practical applications. Before the secrets are shared, each participant is authenticated to ensure the legitimacy of all participants' identities. In addition, the protocol uses single photons as quantum resources, avoiding the complex preparation of entangled states, which makes the protocol more advantageous and potential in practical applications. Security analyses show that the protocol is resistant to collusion attack and other common attacks.

A functional typology of exclusions: why and how women fail to access government schemes in India?

ABSTRACT Social exclusions in India are not solely a result of marginalised, personal identities. They are equally about the failure of the state to recognise those identities. With an expansion of welfare programs, the turf of exclusions is now actively shaped by the state. Drawing upon fieldwork in the drought-prone Marathwada region of Maharashtra (India), this paper outlines the pathways of how citizens fail to access government welfare schemes. The resulting typology of exclusions is a layered one, encompassing failures of state outreach, rejections of citizens’ claims, and inclusions in the schemes coinciding with exclusions from the benefits, fully or partially. Importantly, the exclusions are deeply gendered. During their lifetime, women end up going through multiple cycles of identity verification by the state; pre-marriage, post-marriage and once again, in widowhood or if the marriage were to fail. Ironically, even as the state extends its welfare resources to women through specific schemes, the underlying procedural requirements render women invisible.

Secure multimodal biometric authentication framework with perceptual hashing and lightweight cryptography

Secure multimodal biometric authentication framework with perceptual hashing and lightweight cryptography

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

Indentity Management System Using Blockchain and Survey

Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity

Retraction Note: Quality-based pattern C2 code score-level fusion in multimodal biometric authentication system using pattern net

Retraction Note: Quality-based pattern C2 code score-level fusion in multimodal biometric authentication system using pattern net

AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies.

Conditional Deployable Biometrics: Matching Periocular and Face in Various Settings

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and challenging in-the-wild datasets illustrates its effectiveness in adhering to the CDB paradigm. These datasets encompass a wide range of real-world conditions, further validating the model’s capability to manage variations and complexities inherent in biometric data. The results confirm that CDB-Net not only meets but exceeds expectations in terms of performance, demonstrating its potential for practical deployment in various biometric authentication scenarios.

THE PRINCIPLE OF NEMO TENETUR SE DETEGERE IN THE AGE OF DATA

The objective of the present article is to examine the manner in which the Italian criminal justice system protects the nemo tenetur se detegere principle in the digital era, with a view to providing an insight into this approach for an international audience. In doing so, the article will also consider the experience of other legal systems and the jurisprudence of the courts. In Italy, the nemo tenetur is a right that has its origins in the constitutional protection of the right of defense and the presumption of innocence. There is considerable debate surrounding the extent to which the right should be safeguarded. While the majority of scholarship advocates for the broadest possible extension of the right, this view is not universally accepted: the Cassation court has recently adopted a more lenient approach to the protection of the privilege against self-incrimination, which may permit the use of the suspect's refusal to cooperate as grounds for adverse consequences. The issue arises in instances where law enforcement officials require evidence stored on a laptop or mobile phone that cannot be retrieved without a password, or without the potential for irreparable data loss. Questions arise. Does the obligation to disclose one’s password constitute a form of self-incrimination or a violation of the right to remain silent? Would granting police the power to compel passwords cross a linecenturies old against forcing a person to speak to build the case against them? What are the differences between traditional passwords and biometrical authentication, and what difference does this make for the privilege against self-incrimination?

Improving the Quality of Digital Images on Identity Cards Using Contrast Stretching and Retinex Methods

Enhancing the image quality of the Indonesian Identity Card is crucial to ensure the clarity and accuracy of information in various applications, such as identity verification, administration, and security. This study aims to improve the digital image quality of Identity Card using the Contrast Stretching and Retinex methods. Contrast Stretching is employed to enhance image contrast, while the Retinex method is used to maintain and improve the clarity of colors and image details. The implementation of these methods is carried out using Python. The test results show that these methods are effective in improving the quality of Identity Card images, making the information on the images clearer and easier to read. This research contributes to the development of more advanced image processing technology, which can be applied to various verification and administrative needs that require high reliability.

Railway security checks at the border: between intrusive security technologies and fundamental traveller rights.

European railway borders are facing a particular exposure to security threats and need a delicate balance between securitization and free movement, especially amid globalisation, the current geopolitical landscape and increased migrant flows. For example, the war in Ukraine illustrated the challenges experienced at the Eastern EU borders by the refugee migration surge in early 2022. This exploratory focuses on the European border security control process from the rail border perspective. It encompasses a descriptive synthesis of the lessons learned from the UIC Refugee Task Force as well as insights from the ongoing EU-funded Horizon Europe project ODYSSEUS (Unobtrusive Technologies for Secure and Seamless Border Crossing for Travel Facilitation). Project ODYSSEUS aims to support the security and integrity of the European space, reduce illegal movements of people and goods across EU borders, facilitate travelling for citizens all while protecting fundamental rights of travellers. The project will test a combination of multi-behavioural and General Data Protection Regulation (GDPR) compliant biometric user identity verification tools, allowing, when possible, citizens to cross EU border without any interruption or queue. Further, novel luggage and baggage checks will allow citizens' vehicles and cargos to be remotely checked at land borders to speed up the border check processes in a secure and reliable manner. The project will run three pilot tests at road, rail and water borders. In this paper, we analyse the implementation of project's technologies in the rail border crossing pilot test and discuss the implications for the actors involved in the process of railway border crossing (e.g., border authorities, railway operators and railway travellers).

A privacy protection scheme to resist ring selection attack in peer-to-peer double auction V2V electricity transaction for charging stations

With the development of Electric Vehicles (EVs), the number of public charging piles cannot meet the immediate charging needs of EV users sometimes. The charging stations provide flexible electricity transaction schemes for users by managing charging piles and Vehicle-to-Vehicle (V2V). Users can freely choose transaction partners and submit transaction plans to multiple charging stations. The mobility of EVs results in users randomly selecting rings multiple times, which may cause them to appear in different rings. Attackers can link multiple transaction plans and infer user privacy information. The leakage of transaction information may threaten the fairness of transactions. Aiming at the above situations, a privacy protection scheme to resist ring selection attack in Peer-to-Peer double auction V2V electricity transaction for charging stations is proposed. Firstly, the ring signcryption algorithm is improved to resist ring selection attack. Secondly, a real identity verification algorithm is designed to track malicious users. Theoretical analysis proves that the security of the proposed privacy protection scheme. The elliptic curve point multiplication is used in the scheme to improve computational efficiency. Experiments show that this scheme has low computational and communication costs. Verification cost increases slowly with the increase of the number of ring members.

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life, but it also increases the security problem. Information security has become one of the severe challenges faced by people in the digital age. Currently, the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology, the risk of large-scale data transmission, the potential vulnerabilities introduced by emerging technology, and the dilemma of user identity verification. This paper analyzes the frontier challenges of communication technology and computer network security, and puts forward corresponding solutions, hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

Biometric CNN Model for Verification Based on Blockchain and Hyperparameter Optimization

Nowadays, fingerprints as biometrics are among the most popular means of identity verification for various applications. However, they are susceptible to theft, tampering, or alteration by attackers after storage. Hence, it is critical to guarantee the privacy of these fingerprint templates because standard privacy techniques are not secure enough. Additionally, fingerprint templates are verified using a deep learning model to distinguish between authentic and fake fingerprints, making them more protected and secure by storing them inside a blockchain, which has become the most common secure technique in recent years. This paper implements the proposed efficient and secure biometric system for verification based on blockchain technology and hyperparameter optimization. First, for the storing phase, each user’s authentic fingerprint template, private, and public keys are saved in the block and linked to the previous block in the chain by a hash function. If a hacker attempts to assault a fingerprint, all prior blocks must be changed. Second, for the authentication phase, the user logs in with his fingerprint and looks up the required template in the chain. If the required fingerprint exists, it creates a new block with the login details, and the number 1 is returned, which means that the authentication is valid; if it does not exist, it is verified to see if it is authentic or fake using the proposed biometric convolutional neural network (CNN). The proposed CNN uses the Grid Search (GS) algorithm to tune hyperparameters to distinguish between authentic and fake fingerprints. The SOCOFing dataset is used for evaluating our experiment. According to the experimental results, the proposed CNN model achieved the highest accuracy of 99.52%. As a result of the blockchain, our system can return authentication information after looking up the chain in 300 ms.

Comprehensive Analysis of Bile Medicines Based on UHPLC-QTOF-MSE and Machine Learning.

Based on UHPLC-QTOF-MSE analysis and quantized processing, combined with machine learning algorithms, data modeling was carried out to realize digital identification of bear bile powder (BBP), chicken bile powder (CIBP), duck bile powder (DBP), cow bile powder (CBP), sheep bile powder (SBP), pig bile powder (PBP), snake bile powder (SNBP), rabbit bile powder (RBP), and goose bile powder (GBP). First, 173 batches of bile samples were analyzed by UHPLC-QTOF-MSE to obtain the retention time-exact mass (RTEM) data pair to identify bile acid-like chemical components. Then, the data were modeled by combining support vector machine (SVM), random forest (RF), artificial neural network (ANN), gradient boosting (GB), AdaBoost (AB), and Naive Bayes (NB), and the models were evaluated by the parameters of accuracy (Acc), precision (P), and area under the curve (AUC). Finally, the bile medicines were digitally identified based on the optimal model. The results showed that the RF model constructed based on the identified 12 bile acid-like chemical constituents and random forest algorithm is optimal with ACC, P, and AUC > 0.950. In addition, the accuracy of external identification verification of 42 batches of bile medicines detected at different times is 100.0%. So based on UHPLC-QTOF-MSE analysis and combined with the RF algorithm, it can efficiently and accurately realize the digital identification of bile medicines, which can provide reference and assistance for the quality control of bile medicines. In addition, hyodeoxycholic acid, glycohyodeoxycholic acid, and taurochenodeoxycholic acid, and so forth are the most important bile acid constituents for the identification of nine bile medicines.

Presentation Attack Detection: A Systematic Literature Review

Identity authentication is the process of verifying one’s identity. Many identity authentication methods have been developed, from the conventional username-password systems to the recent electroencephalography-based authentication. Among them, biometric authentication shows particular importance due to its convenience and wide application in real-world scenarios. Face recognition is one of the most widely used biometric authentication methods, but simultaneously it receives various attacks. To overcome attacks, face presentation attack detection has been intensively studied in the last two decades regarding diverse domains of datasets, evaluation methods, and attack types. In this systematic literature review, we identify and categorise the state-of-the-art approaches in each domain to cover the challenges and solutions in a single place. We provide comparisons of representative methods on widely used datasets, discuss their pros and cons, and hope our insights can inspire future works.

HDN-Net: A Hybrid Deep Neural Network to Improve Iris Recognition in Unconstrained Environments with Eyeglasses

Recently, the use of iris recognition technology for biometric authentication has gained widespread acceptance due to the rich texture of the iris region, which provides a reliable standard for recognising individuals, as well as the non-intrusive nature of this method. However, the presence of eyeglasses poses a significant challenge to the accuracy of such systems. In unrestricted environments, current iris recognition techniques cannot effectively extract distinguishing features of the iris. Eyeglasses introduce scratches, specular reflections, dirt, blurriness, and other noise factors over the image of the iris, resulting in low recognition accuracy. To tackle this challenge, researchers have proposed the HDN-Net architecture. This architecture employs a multi-CNN model to combine the features of both the right and left iris images, extracting more distinguishing features to improve the accuracy of the classification task in the presence of challenges caused by eyeglasses. Experiment results show that the proposed iris recognition system achieves more promising performance compared to previous methods used in this field. The overall performance of our suggested HDN-Net method on the UBIRIS.V2 and CASIA-Iris.V4-1000 databases achieved 97.89% and 98.79% accuracy, respectively. Thus, the proposed HDN-Net method consistently outperforms other traditional and deep learning approaches and has the possibility to improve the accuracy of iris recognition systems (IRS) in real-world scenarios.

Enhancing consumer trust in financial services: the role of technological security innovations

The financial services industry increasingly relies on technological advancements to maintain and enhance consumer trust. Consumer trust is a critical component influencing customer loyalty, financial inclusion, and overall economic stability. This paper explores the impact of technological security innovations on consumer trust in financial services. Fundamental innovations such as blockchain, advanced encryption, biometric authentication, artificial intelligence, and cloud security play significant roles in addressing security challenges. Blockchain technology provides a decentralized, tamper-proof method for recording transactions, enhancing transparency and reducing fraud risks. Advanced encryption techniques ensure the confidentiality and integrity of financial data, which is critical for protecting sensitive information. Biometric authentication methods offer secure and user-friendly solutions for identity verification, reducing reliance on traditional passwords and enhancing security. Artificial intelligence and machine learning improve fraud detection capabilities by providing real-time monitoring and response. Cloud security solutions offer scalable protection for financial data, ensuring compliance with regulatory standards. Through empirical analysis and case studies, this study demonstrates that these technologies significantly enhance consumer confidence by providing greater transparency, security, and reliability in financial transactions. The findings offer valuable insights for policymakers, financial institutions, and technology developers aiming to foster a more resilient and trust-based financial ecosystem. This research contributes to the existing literature by providing evidence of the direct impact of technological security innovations on consumer trust. Keywords: Consumer Trust, Financial Services, Technological Innovations, Blockchain, Encryption, Biometric Authentication, Artificial Intelligence, Cloud Security.