Biometric Authentication Research Articles

Re-Lock: Penerapan Integrasi Smart Lock Menggunakan Image Processing Berbasis Artificial Intelligence Menuju Indonesia Emas 2045

Golden Indonesia 2045 is a long-term vision that targets Indonesia to become a developed country with high levels of welfare and security. One important aspect in achieving this vision is the application of advanced technology to support security and efficiency in various sectors. This article aims to discuss the application of smart lock integration using artificial intelligence (AI)-based image processing technology, called “Re-Lock”. Re-Lock technology is designed to enhance physical security through advanced visual identification, allowing access only to authorized individuals based on real-time image analysis. This research adopts a system development method that integrates AI with smart locks, where image processing is used to recognize faces, fingerprints, or certain patterns as a form of identity verification. Case studies were applied to smart home environments and public facilities to test the reliability and efficiency of the system under real conditions. The results show that Re-Lock has a high accuracy rate in identifying individuals and is able to significantly reduce the risk of unauthorized access. In addition, the system is also designed to be easily integrated with existing infrastructure, making it a practical and affordable solution in supporting national security in the digital era. The implementation of Re-Lock is projected to contribute significantly to the achievement of the Golden Indonesia 2045 vision, by making security technology one of the main pillars in the country's development.

Biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics

Biometric authentication has been widely used because of its uniqueness, accuracy, and versatility, but existing methods still have some shortcomings. Because biometric data is stored in the database, once the database is invaded, it will cause a large number of biometric data leakage and illegal use of huge risks; The cost of traditional feature extraction and matching strategy is too high; Biometric is only used for authentication, and its single function cannot meet the increasing security requirements; Methods that rely solely on biometric keys are not secure enough. To overcome these shortcomings, we propose the biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics. The proposed method innovatively combines biometrics and inhomogeneous media optics to achieve dual functions of encryption and authentication. Instead of storing biometric data directly, the encryption and authentication information is encrypted as computer generated holograms in this method and stored in the database to reduce the risk of biometric data leakage. This method has low cost without complicated feature extraction and comparison authentication process. The dual functions of authentication and encryption is more practical. Multiple optics-biology keys improve the security. The security and robustness are analyzed. A graphical user interface (GUI) is designed for the method to promote its application. This work provides new methods and new ideas for the field of optics encryption and authentication, and will certainly have a broad application prospect.

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones

As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.

Development of an Electronic Voting System using Blockchain Technology and Deep Hybrid Learning

Democratic people cannot function properly in today's sophisticated societies (where voting is a prominent issue) without electronic voting technologies. This study explores the use of hybrid learning algorithms for biometric authentication of voters, and blockchain technology for secure electronic voting. The thorough analysis includes a collection of more than 50,000 fingerprint samples using custom Convolutional Neural Network (CNN), VGG16, VGG19, Xception, and Inception. The algorithms are evaluated using F1-score, recall, accuracy, and precision. By combining Random Forest with a specially designed CNN, a novel hybrid learning algorithm is developed for authentication purposes. This blended model provides the best outcome in terms of accuracy (99.32%) and precision (99.32%). In addition, a web application was developed. This application integrates blockchain technology for electronic voting using Flask, HTML, and Solidity. By using blockchain, tampering and unauthorized access are prevented. It also ensures impartial voting and secure storage. The tabular presentation of the results provides a clear summary of each candidate's total votes.

Anti Face Spoofing System

Face spoofing, the act of using deceptive techniques such as printed photos or digital images to deceive facial recognition systems, poses a significant threat to the security of various applications, including biometric authentication and access control systems. This paper presents a concise yet effective approach to address the challenge of anti-face spoofing using Python within a limited codebase of 50 lines. The proposed solution leverages a combination of image processing techniques and machine learning algorithms to detect and prevent face spoofing attempts. A pre-trained deep neural network model for facial recognition is employed to extract essential facial features. Subsequently, the system utilizes image manipulation detection methods to identify anomalies indicative of face spoofing attacks. The implementation showcases the simplicity and efficiency of the proposed anti-face spoofing technique, demonstrating the potential for integration into real- world applications with minimal computational overhead. The concise Python code enables easy adoption and adaptation for developers aiming to enhance the security of facial recognition systems against face spoofing threats. The experimental results demonstrate the effectiveness of the approach in accurately distinguishing between genuine and spoofed facial images, thereby contributing to the robustness of facial recognition systems in the presence of adversarial attacks. Keywords - Liveness Detection, Presentation Attack Detection, Spoof Detection Algorithms, Biometric Security, Face Recognition Robustness, 3D Face Modeling, Texture Analysis, Deep Learning Anti- Spoofing, Multi-Spectral Imaging, Behavioral Biometrics, Challenge-Response Mechanisms, Optical Flow Analysis, Pulse Detection, Surface Reflectance, Depth Sensing, Motion Analysis, Pattern Recognition, Anti-Spoofing Networks, Temporal Feature Extraction, Anomaly Detection.

The Role of QR Code Technology in Revolutionizing Banking

The financial services industry has undergone a significant digital revolution, with QR code technology playing a crucial role in the development of the banking sector. Evolving from basic barcodes, QR codes have become essential tools for identity verification, secure online transactions, and financial account management. They represent a shift from conventional banking practices, offering enhanced security and user convenience. In banking, QR codes are used for account management, payments, and robust security protocols. The widespread adoption of QR codes has transformed how customers interact with banks and has led to stronger security measures for online banking. This article examines the growing use of QR codes in banking, highlighting the convenience, security, and improved customer experience these codes offer. The increasing importance of QR technology in banking is further demonstrated by emerging trends such as Data Matrix QR Codes, Blockchain-Enabled QR Codes, Secure Timestamping, Encryption, and Dynamic QR Codes.

A proposed biometric authentication hybrid approach using iris recognition for improving cloud security

Biometric systems have gained attention as a more secure alternative to traditional authentication methods. However, these systems are not without their technical limitations. This paper presents a hybrid approach that combines edge detection and segmentation techniques to enhance the security of cloud systems. The proposed method uses iris recognition as a biometric paradigm, taking advantage of the iris' unique patterns. We performed feature extraction and classification using hamming distance (HD) and convolutional neural networks (CNN). We validated the experimental findings using various datasets, such as MMU, IITD, and CASIA Iris Interval V4. We compared the proposed method's results to previous research, demonstrating recognition rates of 99.50 % on MMU using CNN, 97.18 % on IITD using CNN, and 95.07 % on CASIA using HD. These results indicate that the proposed method outperforms other classifiers used in previous research, showcasing its effectiveness in improving cloud security services.

Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

With the evolution of next-generation communication networks, ensuring robust Core Network (CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service (DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture (BECNA) and the Secure Decentralized Identity Authentication Scheme (SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure (SPoF). The SDIDAS utilizes Decentralized Identity (DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

Facial recognition - A literature review

This paper analyses the main technologies for face recognition, a critical biometric tool for identity verification and security across various sectors. A comprehensive overview of traditional and modern facial recognition technologies will be provided, examining their key features such as age, pose, and illumination. The study discusses the evolution and current state of facial recognition, highlighting significant advancements and applications in recent years. The objective is to offer a detailed understanding of how these technologies function and their implications for security and identity verification.

Empowering Fashion Design and Intelligent Manufacturing with Digital Twins in the Metaverse Era

In the era of metaverse, digital twin technology is not only applied to traditional industrial and aerospace fields but also begins to empower fashion design and intelligent manufacturing, ushering in a new business model and a new era of value creation. This article discusses the application of digital twin technology in the fashion industry and the digital value it brings. In the fashion industry, digital twin technology is mainly applied in five areas: the design and development of fashion products, identity verification, tracking the entire lifecycle of fashion products, intelligent manufacturing of digital twin clothing factories, and immersive experience of fashion display. In addition, digital twin technology realizes the digital value added to clothing by connecting to NFT ecosystems, games, and other emerging fields. On the one hand, the value of physical clothing is enhanced by using physical products plus digital products; on the other hand, digital-twin clothing as independent digital assets opens up new markets and business models, providing new ways for brands to extend their influence into the virtual world. Digital twin technology has revolutionized the traditional mode of the fashion industry, created new business opportunities and value growth points for it, and will further promote the industry's digital transformation and intelligent development in the future.

Biometric based payment system

The increasing need for secure and convenient payment methods has fueled the exploration of biometric authentication systems. This paper presents a Biometric Based Payment System designed to streamline transactions while prioritizing security. The system employs a fingerprint sensor (R307) to ensure robust user identification, complemented by a 4x4 keypad for versatile input and an I2C OLED display for clear feedback. At its core, the system leverages an ESP32 microcontroller and robust SHA-256 encryption to protect sensitive information during storage and transmission. Transaction data resides in a centralized MySQL database on an Apache server, and users manage payment history through a web interface. The system emphasizes security and ease of use, with the potential for future exploration of multi-factor authentication, offline capabilities, and blockchain integration to further enhance its robustness

Blockchain technology in financial services: enhancing security, transparency, and efficiency in transactions and services

Blockchain technology revolutionizes financial services by enhancing security, transparency, and efficiency in transactions and services. This paper examines the fundamental aspects of blockchain, including its core concepts and components, and explores its application in financial services. Blockchain's cryptographic hashing, decentralization, and immutability significantly improve security by preventing tampering and reducing fraud risks. Increased transparency through immutable audit trails, real-time updates, and open ledgers fosters trust among customers, regulators, and institutions. Furthermore, blockchain streamlines operations by automating processes, reducing intermediaries, and expediting transactions, resulting in substantial cost reductions and improved efficiency. Despite these advantages, challenges such as scalability, interoperability, regulatory uncertainty, and environmental impact must be addressed for widespread adoption. The prospects of blockchain in financial services are promising, with potential applications in decentralized finance, digital identity verification, and central bank digital currencies. This paper underscores the transformative potential of blockchain technology in creating a more secure, transparent, and efficient financial ecosystem.

Digital identities in the metaverse: Privacy, security, and user authentication in virtual financial systems

The advent of the metaverse has ushered in a new era of digital interaction, commerce, and finance. As users increasingly immerse themselves in virtual spaces, the need for robust digital identities becomes paramount. This study investigates the intricate interplay of privacy, security, and user authentication within the context of digital identities in the metaverse, with a particular focus on virtual financial systems. Employing a cross-case analysis method within the qualitative research paradigm, the study extensively examines real-world instances to extract valuable insights, identify challenges, and propose potential solutions in navigating this dynamic landscape. This approach allows for a comprehensive examination of overarching patterns and trends across case studies, revealing persistent challenges in digital identity management. The study delves into the complexities of pseudonymity in virtual engagement, emphasizing the crucial balance required for user trust and accountability. Furthermore, it uncovers commonalities in successful implementations, spotlighting the transformative role of blockchain technology, exemplified by IBM Blockchain World Wire, and the growing trend of biometric authentication, as seen in the FIDO use case. The successful cases examined demonstrate that a user-friendly experience and scalability are integral to the adoption of advanced security technologies. Industry leaders should take note of the global trends in digital transactions, emphasizing efficiency, transparency and cross-border security. Additionally, the study offers forward-looking insights with implications for future research and practice, advocating for continued exploration of the nuanced dynamics between user privacy, trust-building mechanisms and accountability in the ever-evolving metaverse landscape.

Speaker Recognition: A Comparative analysis Between Deep Learning and Non-Deep Learning Methodologies

This work carries out a comparative study of two methodologies for speaker recognition. It is Deep Learning (DL) and Vector Quantization (VQ). The key area in biometric au-thentication systems involves speaker recognition. This requires durable and efficient algorithms. The aim is to ensure a high accuracy and reliability. The study delves into a deep neural network (DNN) model implementation. It leverages advanced feature extraction. It uses pattern recognition capabilities which are in DL. The study also examines a traditional VQ approach. This method makes use of codebook generation and quantization for speaker ID. Extensive experimentation was done on standard datasets. The project evaluates the performance of two methods. It compares accuracy. It assesses computational complexity. It does so for noise and for variations in speech. The findings of this analysis reveal the strengths and limitations of each technique. Looking at their practical applicability in real-world scenarios provides insights. The comparative results of these techniques aim to guide future developments. This concerns speaker recognition systems - particularly their potential for enhanced performance.

Biometrics and Password Less Authentication: The Future of Digital Security

Biometrics and Password Less Authentication: The Future of Digital Security

DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition.

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.

Multi-factor Authentication Framework for Secured Data Access in Cloud Computing

Aims and Background: Accessing data services on the cloud raises serious concerns about security and privacy. To verify a client's stated identity, the existing authentication approach uses just one authentication factor. However, traditional techniques of one-factor authentication are inefficient since they demand an excessive amount of user information and expose their private data to eavesdropping. Methods: Cloud-based data storage is susceptible to both external and internal threats. Information security relies heavily on authentication, which is a method for verifying user identities before granting them access to protected data. When it comes to protecting data in a cloud computing environment from the most cutting-edge methods of attack, traditional password authentication simply isn't enough. For the purpose of protecting user identities in the cloud, this study presents interactive multi-layer authentication systems. Various access control policies are used when validating users in the cloud. Registration, permissions, and authentication factors for users are all parts of the cloud app's security process. Results: An intrusion detection system is built into the security mechanism to help identify people who could be malevolent. Various techniques are available for user identity verification, including intrusion detection, access control, and multi-factor authentication. The last step is to encrypt the data so that no one else can read it. Conclusion: To guarantee the effectiveness and accuracy of the suggested frameworks and methodology, they are experimentally evaluated. There were few false positives and a high detection rate.

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Unlocking Security: The Significant Role of Machine Learning and Deep Learning Techniques in Fingerprint Recognition Systems

One of the more widely utilized biometrics for in-person identification is a fingerprint. It has been discovered that no two people have the same fingerprints and that each one is distinct. A person's fingerprint traits remain constant as they age. Compared to DNA, fingerprints are more distinctive. Identical twins cannot have the same fingerprints even though they have the same DNA. Artificial Intelligence encompasses the broader goal of creating intelligent systems, while Machine Learning focuses on developing algorithms that enable machines to learn from data. Deep Learning, as a subset of Machine Learning, leverages neural networks with multiple layers to learn complex representations of data. While Deep Learning has become increasingly prominent and successful in recent years, it is just one of the many approaches within the broader field of AI and Machine Learning. Fingerprint recognition stands as one of the oldest and most widely used biometric authentication methods, finding applications across diverse domains such as law enforcement, access control, and mobile device security. With the advent of machine learning and deep learning techniques, significant strides have been made in enhancing the accuracy, efficiency, and scalability of fingerprint recognition systems. This comprehensive review aims to provide a thorough examination of the pivotal role played by machine learning and deep learning methodologies in advancing fingerprint recognition technology. The paper commences with an introduction to the importance and ubiquity of fingerprint recognition, elucidating its significance in various real-world applications. Subsequently, it delves into the foundational concepts of machine learning and deep learning, elucidating their relevance to fingerprint recognition tasks. Key machine learning algorithms such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Random Forests, and neural network-based approaches are discussed, highlighting their strengths and limitations in the context of fingerprint recognition.

Fingerphoto morphing attack generation using texture descriptors based landmarks

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.