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1

Sobabe, Abdou-Aziz, Tahirou Djara, and Antoine Vianou. "Biometric System Vulnerabilities: A Typology of Metadata." Advances in Science, Technology and Engineering Systems Journal 5, no. 1 (2020): 191–200. http://dx.doi.org/10.25046/aj050125.

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2

Singh, Yogendra Narain, and Sanjay Kumar Singh. "A taxonomy of biometric system vulnerabilities and defences." International Journal of Biometrics 5, no. 2 (2013): 137. http://dx.doi.org/10.1504/ijbm.2013.052964.

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3

Kumar, Govind, Mozakker Anam, Md Sajid, and Dr Sakthivel M. "Finger Print Voting System." International Research Journal of Computer Science 11, no. 04 (2024): 200–203. http://dx.doi.org/10.26562/irjcs.2024.v1104.11.

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The field of biometrics, or the identification of people based on physiological or behavioral characteristics [6], has recently received a significant amount of attention in current research as a result of its applicability in the fields of information technology and security. With the increased usage of PIN numbers and passwords in everyday life, the vulnerabilities of these two technologies are becoming more apparent (e.g. password-cracking). Biometric authorization’s strengths are that it requires the user to be present and that it eliminates the hassles of passwords and PIN’s (or can be used in parallel with these for added security). Specifically, online identity verification, ATM machines, and building entrance authorization, are all areas where it is very useful to implement an automatic identification system.
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4

S Prasad, Puja. "Vulnerabilities of Biometric Authentication Systems: A Survey." HELIX 8, no. 5 (2018): 4100–4103. http://dx.doi.org/10.29042/2018-4100-4103.

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5

Moorthy, Kohbalan, Kauthar Mohd Daud, Steven R. Arokiasamy, and Md Raihanul Islam Tomal. "HYBRID BIOMETRIC AUTHENTICATION FOR AUTOMATIC TELLER MACHINE." International Journal of Software Engineering and Computer Systems 10, no. 1 (2024): 32–39. http://dx.doi.org/10.15282/ijsecs.10.1.2024.3.0121.

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Automated Teller Machines (ATMs) are a crucial part of modern life, providing users with the ability to conduct basic banking transactions electronically without the need for bank representatives. These machines are ubiquitous, particularly in developed countries, and their usage is expanding rapidly across the globe. However, this widespread adoption has highlighted significant security concerns for general users. Traditional security measures, such as Personal Identification Numbers (PINs), are increasingly proving insufficient due to issues like card theft, ATM scams, and other security breaches. To address these vulnerabilities, biometrics-based authentication is emerging as a promising alternative. Unlike traditional password or PIN-based methods, biometric authentication uses unique physical characteristics, such as fingerprints, to verify identity. This method offers a higher level of security by making it significantly harder for unauthorized users to access accounts. In this research, we introduce a hybrid biometric authentication system for ATMs, focusing on fingerprint recognition. Our prototype aims to enhance transaction security and provide a more user-friendly experience. By integrating biometric strategies with ATMs for single verification, we aim to reduce the risks associated with PIN-based authentication and improve overall security. This approach not only addresses existing vulnerabilities but also sets the stage for more secure and reliable ATM transactions in the future.
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Gupta, Sharad. "Anti-Spoofing: Liveness Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 4997–5003. https://doi.org/10.22214/ijraset.2025.70787.

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Abstract: Face recognition systems became more susceptible to presentation attacks by digital screens, printed images, and 3D masks [3]. This paper introduces a full-fledged anti-spoofing solution based on the YOLO (You Only Look Once) frameworktoidentifyandthwart suchattemptsatspoofinginreal-time[14]. Oursystemintegrates effective object detection features with custom liveness evaluation features to form an effective security layer for biometric authentication systems. Experimental results show high accuracy in distinguishing between real users and spoofing attempts with real-time performance appropriate for practical use [4]. The study points out the efficiency of feature extraction from biometric informationusingCNNs [16] and the capacity ofTransformers to model global dependencies for improvedspoof detection [11]. By combining these approaches, the study seeks to enhance the accuracy and reliability of liveness detection, mitigating vulnerabilities in biometric authentication systems [9].
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Kancherla, Tarun. "Fingerprint-Based Voting System Using C#: A Secure Biometric Approach to Modern Elections." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 158–60. https://doi.org/10.22214/ijraset.2025.68176.

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Integrity in elections is fundamental to democratic processes, yet traditional voting systems face significant vulnerabilities such as rigging and voter fraud. This paper introduces a biometric voting so- lution leveraging fingerprint recognition to enhance electoral transparency developed using C# and SQL Server Studio to improve electoral transparency and security. The proposed system employs bio- metric authentication to uniquely identify voters, effectively eliminating impersonation and multiple voting incidents. System performance evaluations demonstrate a biometric matching accuracy of 98%, underscoring the practicality and efficiency of biometric systems in real- world electoral environments.
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Khranovskyi, Mykola, and Andriy Kernytskyy. "Blockhain and Biometrics Challenges and Solutions." Computer Design Systems. Theory and Practice 6, no. 1 (2024): 189–98. http://dx.doi.org/10.23939/cds2024.01.189.

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Blockchain technology has garnered significant attention in recent years due to its ability to revolutionize conventional processes by providing faster, more secure, and cost-effective solutions. This study explores the symbiotic relationship between blockchain and biometrics, investigating how these technologies can mutually reinforce each other. The research makes a dual contribution: firstly, it comprehensively analyses blockchain and biometrics, highlighting their convergence's potential advantages and obstacles. Secondly, it delves deeper into utilising blockchain for safeguarding biometric templates. Although the potential benefits outlined earlier are promising, integrating blockchain and biometric technologies faces challenges due to constraints within current blockchain technology. These constraints include a limited transaction processing capacity, the need to store all system transactions leading to increased storage demands, and insufficiently explored resilience against diverse attacks. Historically, biometric systems have been vulnerable to both physical and software-based attacks. While techniques like presentation attack detection can somewhat mitigate physical sensor vulnerabilities, safeguarding against software attacks necessitates adopting biometric template protection measures. Despite advancements in this area, there remains scope for enhancing these methods. Integrating blockchain and biometrics promises to enhance security and efficiency across various sectors. By combining blockchain's immutability and transparency with biometric data's uniqueness and reliability, organizations can establish robust systems that protect sensitive information while streamlining processes. This research underscores the importance of understanding the intricacies of merging these technologies to leverage their full potential effectively. Overall, this study sheds light on the transformative power of integrating blockchain and biometrics, offering insights into how this synergy can drive innovation, improve security measures, and optimize operations in a rapidly evolving digital landscape.
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9

Mishra, Shalabh Kumar, Satyvir Singh, Ashu Soni, et al. "Development and deployment of a biometric fingerprint lock system." Journal of Information and Optimization Sciences 46, no. 1 (2025): 253–62. https://doi.org/10.47974/jios-1869.

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Security remains a paramount concern in both residential and office settings, prompting the exploration of various solutions. Existing door lock systems often exhibit vulnerabilities that compromise security, raising apprehensions about a safe lifestyle and conducive work environment. Moreover, contemporary challenges, such as terrorism and unauthorized access, underscore the urgency of implementing robust security measures, especially in shared-access spaces. This paper introduces the design and prototype of a biometric fingerprint door lock system tailored for joint access environments. Fingerprint biometrics, known for their reliability, offer a secure means to log system transactions and safeguard individual privacy rights. In contrast to RFID or password-based mechanisms susceptible to compromise, the proposed system relies solely on fingerprint verification, providing enhanced security for shared facilities. The system, built on an Arduino UNO platform, incorporates a fingerprint sensor, camera, servo motor, buzzer, and an LCD display. Authorized users’ fingerprints are enrolled and verified, granting access to shared facilities, while centralized control facilitates user management. This flexible device ensures physical security by leveraging fingerprint sensor technology.
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10

Shuwandy, Moceheb Lazam, Rawan Adel Fawzi Alsharida, and Maytham M. Hammood. "Smartphone Authentication Based on 3D Touch Sensor and Finger Locations on Touchscreens via Decision-Making Techniques." Mesopotamian Journal of CyberSecurity 5, no. 1 (2025): 165–77. https://doi.org/10.58496/mjcs/2025/011.

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Smartphone authentication systems must balance security and user convenience, which is a persistent challenge in the digital realm. Traditional biometrics, such as fingerprints and facial recognition, face vulnerabilities to spoofing and environmental conditions, limiting reliability. This study introduces a novel approach by integrating three-dimensional (3D) touch sensors with finger location data for authentication. The goal is to develop a system that improves accuracy while minimizing false positives and negatives, leveraging touch pressure and spatial interaction as unique biometric identifiers. Data from 20 participants, including pressure levels, spatial coordinates, and timestamps, were analysed using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that combining pressure sensitivity with spatial data significantly improved performance, achieving an F1- score of 0.83 and an accuracy of 83%. The system demonstrated balanced precision (0.84) and recall (0.83), effectively reducing false positives and negatives. Robustness was confirmed through cross-validation tests, which validated the consistency across datasets and real-time usability scenarios. This study establishes a foundation for secure, user-friendly smartphone authentication, highlighting the potential of 3D touch technology in addressing current biometric system limitations. This approach opens avenues for further research in mobile security, integrating multimodal biometric data with advanced machine learning techniques.
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V.K., Narendira Kumar, and Srinivasan B. "DESIGN AND IMPLEMENTATION OF E-PASSPORT SCHEME USING CRYPTOGRAPHIC ALGORITHM ALONG WITH MULTIMODAL BIOMETRICS TECHNOLOGY." International Journal of Advanced Information Technology (IJAIT) 1, no. 6 (2011): 33–42. https://doi.org/10.5281/zenodo.3406965.

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Advancements in technology have created the possibility of greater assurance of proper travel document ownership, but some concerns regarding security and effectiveness remain unaddressed. Electronic passports have known a wide and fast deployment all around the world since the International Civil Aviation Organization the world has adopted standards whereby passports can store biometric identifiers. The use of biometrics for identification has the potential to make the lives easier, and the world people live in a safer place. The purpose of biometric passports is to prevent the illegal entry of traveler into a specific country and limit the use of counterfeit documents by more accurate identification of an individual. This paper analyses the face, fingerprint, palmprint and iris biometric e-passport design. This papers focus on privacy and personal security of bearers of e-passports, the actual security benefit countries obtained by the introduction of e-passports using face, fingerprint, palmprint and iris recognition systems. Researcher analyzed its main cryptographic features; the face fingerprint, palmprint and iris biometrics currently used with e-passports and considered the surrounding procedures. Researcher focused on vulnerabilities since anyone willing to bypass the system would choose the same approach. On the contrary, solely relying on them may pose a risk that did not exist with previous passports and border controls. The paper also provides a security analysis of the e-passport using face fingerprint, palmprint and iris biometric that are intended to provide improved security in protecting biometric information of the e-passport bearer.
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12

Acar, Abbas, Shoukat Ali, Koray Karabina, et al. "A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA." ACM Transactions on Privacy and Security 24, no. 4 (2021): 1–28. http://dx.doi.org/10.1145/3464690.

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As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, <underline>p</underline>rivacy-<underline>a</underline>ware, and secure <underline>c</underline>ontinuous <underline>a</underline>uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.
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13

Tyagi, S., M. Kumar, D. Singh, and H. Tyagi. "Biometric Cryptosystem Based On Fingerprint Authentication And Cryptography Technique." International Research Journal of Parroha Multiple Campus (IRJPMC) 1, no. 1 (2023): 26–39. https://doi.org/10.5281/zenodo.10258787.

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<i>Biometric systems are one of the most reliable and popular techniques today and fingerprint authentication is one of the most reliable and robust biometric techniques due to its nature. The characteristics of fingerprints play a big and important role in the authentication of people. In this research, fingerprint authentication scheme consists of many stages: image enhancement, binarization, segmentation, spine thinning, detail extraction. In this authentication we use Gaussian filter for better result. Hybrid protection is created through a combination of biometrics and cryptography, such as fingerprint and cryptography schemes. The combination of many biometric features with a single crypto key should offer an approach to increase authenticity and reduce the fake acceptance rate (FAR) and fake rejection rate (FRR) of fingerprints. For each new user of a biometric system, the combination of a cryptobiometric system will overcome the limitations of accuracy and vulnerabilities. We want to protect our real data from unauthorized people and systems, so we use cryptographic schemes as Elliptic Curve Diffie Hellman's key exchange algorithm. Biometric techniques can be used for various applications, such as: Biometrics can help make processes, transactions and everyday life safer and more convenient. You can use biometric data anywhere. to provide a valid identity solution. Cryptographic systems and fingerprint authentication have been identified as two of the most important aspects of the security environment. In this document, two powerful techniques are combined to produce better and safer results. In this study we use Gaussian filters, because less FAR and less FRR, with fingerprints authorized and finally authentication being a security key or a secure message created for a particular job . If the entered fingerprint matches the authorized person, but the DBA fingerprint does not, the system says "You are an unauthorized person, please try again." If the two fingerprints match, it will send all the secure passwords or cryptographic keys or secure messages for each work. It is developed by MATLAB (Matrix Laboratory). The proposed algorithm was tested on the FVC2004 database and compared with all participants in FVC2004.</i><i><strong>Keyword:&nbsp;</strong>Biometric systems, Fingerprint authentication, Image enhancement, Cryptography, Gaussian filter, FAR, and FRR</i>
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14

Dorofeev, K. A. "COMPARATIVE ANALYSIS OF VULNERABILITIES IN BIOMETRIC FACE RECOGNITION SYSTEMS." Journal of the Ural Federal District. Information security 22, no. 3 (2022): 34–46. http://dx.doi.org/10.14529/secur220304.

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15

Ghilom, Milkias, and Shahram Latifi. "The Role of Machine Learning in Advanced Biometric Systems." Electronics 13, no. 13 (2024): 2667. http://dx.doi.org/10.3390/electronics13132667.

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Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users’ changing biometric patterns, ML algorithms can improve accuracy and performance over time. The integration of ML algorithms with biometrics, however, introduces vulnerabilities in such systems. This article investigates the new issues of concern that come about because of the adoption of ML methods in biometric systems. Specifically, techniques to breach biometric systems, namely, data poisoning, model inversion, bias injection, and deepfakes, are discussed. Here, the methodology consisted of conducting a detailed review of the literature in which ML techniques have been adopted in biometrics. In this study, we included all works that have successfully applied ML and reported favorable results after this adoption. These articles not only reported improved numerical results but also provided sound technical justification for this improvement. There were many isolated, unsupported, and unjustified works about the major advantages of ML techniques in improving security, which were excluded from this review. Though briefly mentioned, we did not touch upon encryption/decryption aspects, and, accordingly, cybersecurity was excluded from this study. At the end, recommendations are made to build stronger and more secure systems that benefit from ML adoption while closing the door to adversarial attacks.
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N, Ramya. "Biometric Empowered Swiping Machine." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47614.

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Abstract - The Biometric Empowered Swiping Machine is an innovative solution aimed at enhancing authentication security by integrating fingerprint recognition technology with traditional card-swiping systems. By utilizing biometric characteristics that are unique to each individual, the machine eliminates common vulnerabilities associated with traditional authentication methods such as PINs and magnetic stripe cards. This project is designed for diverse applications including corporate offices, educational institutions, public transport, and banking sectors. Through the adoption of deep learning algorithms, secure data encryption, and cloud storage, the system ensures accuracy, convenience, and fraud prevention. This report discusses the design, methodology, implementation, and future enhancements of the Biometric Empowered Swiping Machine. A Biometric Empowered Vending Machine revolutionizes the traditional vending experience by integrating advanced biometric authentication technologies, such as fingerprint, facial recognition, or iris scanning. This innovative system ensures secure and convenient transactions, eliminating the need for cash, cards, or memorized PINs. Upon authentication, users can select their preferred products, and the machine accurately dispenses the chosen items. Key Words:Biometric,Swiping Machine,PINs,Vending Machine
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Gowda, V. Dankan, Pratik Gite, Mirzanur Rahman, Kirti Rahul Kadam, G. Manivasagam, and K. D. V. Prasad. "Novel approaches to biometric security using enhanced session keys and elliptic curve cryptography." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 2 (2024): 477–88. http://dx.doi.org/10.47974/jdmsc-1884.

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Biometric systems have taken the front seat as having core foundations in 21stcentury digital information technology, biometric systems hold a position therein or authentication processes. But the growing complexity of cyber threats requires greater security practices. This paper presents an innovative biometric security approach based on the combination of ECC and more advanced session keys. The goal is to strengthen biometric systems against sophisticated cyber threats ensuring fast and effective authentication methods. The proposed approach takes advantage of ECC’s strength in generating secure biometric data along with session keys dynamically to build a more resilient, yet flexible, security model. It is shown on the simulations that security metrics increase significantly in terms of resistance to common cryptographic attacks and data breaches without decreasing the system performance. This study describes not only the current vulnerabilities in biometric security systems towards more secure and reliable authentication systems by the convergence of biometric data to advanced cryptographic techniques.
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Rodiah, Sarifuddin Madenda, Diana Tri Susetianingtias, Fitrianingsih, Dea Adlina, and Rini Arianty. "Retinal biometric identification using convolutional neural network." Computer Optics 45, no. 6 (2021): 865–72. http://dx.doi.org/10.18287/2412-6179-co-890.

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Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.
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T, Maheswaran, Dinesh M, Guhan S, Kamalakar L, and Mohammed Abdulla M. "Real Time Face Recognition Security System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43520.

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This project develops a face recognition-based door unlocking system with a backup keypad for added security. It uses a Raspberry Pi 3B+ to process real-time facial recognition for primary authentication. If face recognition fails, users can enter a pre-set password on the keypad as a secondary authentication method. A stepper motor controls the locking mechanism for door access. The system enhances security by combining biometric authentication with a dual-layer approach. It provides a secure, contactless, and user-friendly access solution. The project addresses the vulnerabilities of traditional locking mechanisms like key duplication and combination lock guessing. It aims to improve both security and usability in environments where access control is critical. The system is designed to be reliable and practical for everyday use. This approach offers a modern solution to traditional door security challenges. Keywords: Raspberry Pi, Biometric authentication, Face recognition
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Mukul Jangid, Surbhi Gupta, and Shubham Sharma. "Zero trust biometric attendance: A secure face recognition framework." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2437–49. https://doi.org/10.30574/wjaets.2025.15.2.0807.

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Automated attendance systems using face recognition present significant privacy challenges that require urgent attention due to their widespread adoption in academic and corporate environments. This research develops and evaluates a secure attendance system that implements AES-256 encrypted biometric storage in Database, addressing critical vulnerabilities in conventional approaches. The proposed solution combines hybrid encryption (AES+Fernet) with dynamic initialization vector generation and role-based access control to ensure GDPRcompliant data handling. Through rigorous testing, the system achieves 98.2% recognition accuracy with 290ms average processing time while reducing privacy risks by 89% compared to unencrypted systems. The architecture prioritizes three key aspects: (1) computational efficiency for real-time deployment, (2) robust security through multi-layered encryption, and (3) practical implementation simplicity. By comparing various encryption strategies and storage approaches, this study identifies optimal configurations that balance performance with privacy protection. The findings demonstrate that proper cryptographic implementation can maintain high recognition accuracy while eliminating common biometric data vulnerabilities. This research provides valuable insights for both system administrators and security practitioners, establishing a framework for developing privacy-preserving attendance systems. The results highlight the feasibility of implementing military-grade encryption without compromising operational efficiency, offering actionable guidelines for organizations transitioning from traditional attendance methods. Furthermore, the study underscores the importance of continuous security enhancements to address evolving threats in biometric data management.
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Tyagi, Shivani, Manish Kumar, Dharambeer Singh, and Hariom Tyagi. "Biometric Cryptosystem Based On Fingerprint Authentication And Cryptography Technique." International Research Journal of Parroha Multiple Campus 1, no. 1 (2022): 26–39. http://dx.doi.org/10.61916/prmn.2023.v0101.004.

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Biometric systems are one of the most reliable and popular techniques today and fingerprint authentication is one of the most reliable and robust biometric techniques due to its nature. The characteristics of fingerprints play a big and important role in the authentication of people. In this research, fingerprint authentication scheme consists of many stages: image enhancement, binarization, segmentation, spine thinning, detail extraction. In this authentication we use Gaussian filter for better result. Hybrid protection is created through a combination of biometrics and cryptography, such as fingerprint and cryptography schemes. The combination of many biometric features with a single crypto key should offer an approach to increase authenticity and reduce the fake acceptance rate (FAR) and fake rejection rate (FRR) of fingerprints. For each new user of a biometric system, the combination of a cryptobiometric system will overcome the limitations of accuracy and vulnerabilities. We want to protect our real data from unauthorized people and systems, so we use cryptographic schemes as Elliptic Curve Diffie Hellman's key exchange algorithm. Biometric techniques can be used for various applications, such as:Biometrics can help make processes, transactions and everyday life safer and more convenient. You can use biometric data anywhere. to provide a valid identity solution. Cryptographic systems and fingerprint authentication have been identified as two of the most important aspects of the security environment. In this document, two powerful techniques are combined to produce better and safer results. In this study we use Gaussian Tyagi, S., Kumar, M., Singh, D. &amp; Tyagi, H. (2023). IRJPMC; 1(1)– 27– filters, because less FAR and less FRR, with fingerprints authorized and finally authentication being a security key or a secure message created for a particular job . If the entered fingerprint matches the authorized person, but the DBA fingerprint does not, the system says "You are an unauthorized person, please try again." If the two fingerprints match, it will send all the secure passwords or cryptographic keys or secure messages for each work. It is developed by MATLAB (Matrix Laboratory). The proposed algorithm was tested on the FVC2004 database and compared with all participants in FVC2004. Keyword: Biometric systems, Fingerprint authentication, Image enhancement, Cryptography, Gaussian filter, FAR, and FRR.
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22

YEZHOVA, Y. "MULTIMODAL NEURAL NETWORK USER AUTHENTICATION SYSTEMS BASED ON BIOMETRIC FEATURES." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 1, no. 40 (2025): 40–50. https://doi.org/10.31474/1996-1588-2025-1-40-40-50.

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"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"
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Muhammad Saad Zahoor, Et al. "Biometric Encryption: Integrating Artificial Intelligence for Robust Authentication." Dandao Xuebao/Journal of Ballistics 35, no. 3 (2023): 25–33. http://dx.doi.org/10.52783/dxjb.v35.121.

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Biometric authentication, leveraging unique physiological or behavioral traits for identity verification, has emerged as a cornerstone of contemporary security systems. However, the increasing sophistication of cyber threats and the potential vulnerabilities of biometric data demand continuous innovation to fortify authentication mechanisms. This research paper delves into the intricate integration of Artificial Intelligence (AI) with biometric encryption systems to elevate authentication robustness to unprecedented levels. The pursuit of enhanced security in biometric authentication systems is motivated by the escalating need to safeguard sensitive personal information from unauthorized access and malicious exploitation. Current biometric systems, though effective, face challenges such as spoofing, replay attacks, and the risk of biometric data compromise. [1]The introduction of AI into this paradigm offers a transformative approach, aiming not only to overcome these challenges but also to adapt and evolve in response to emerging threats. The objectives of this research encompass a comprehensive evaluation of existing biometric authentication systems, the exploration of potential advantages stemming from the infusion of AI, the development of a prototype system exemplifying AI-integrated biometric encryption, and a meticulous assessment of its performance through experimentation and analysis. As the paper concludes, it not only summarizes the key discoveries but also underscores the broader implications for the field of biometric authentication. The fusion of biometric encryption and AI not only fortifies security but also sets the stage for future innovations, shaping the landscape of secure and reliable authentication mechanisms in an increasingly digital world.
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Nita, Stefania, Marius Mihailescu, and Valentin Pau. "Security and Cryptographic Challenges for Authentication Based on Biometrics Data." Cryptography 2, no. 4 (2018): 39. http://dx.doi.org/10.3390/cryptography2040039.

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Authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of the society, e.g., Smart City, Internet-of-Things (IoT), Cloud Computing, Big Data. In the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc. Due to its uniqueness, characteristic, biometrics systems will become more and more vulnerable, privacy being one of the most important challenges. The classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy. The current paper has several objectives. The main objective consists in creating a framework based on cryptographic modules which can be applied in systems with biometric authentication methods. The technologies used in creating the framework are: C#, Java, C++, Python, and Haskell. The wide range of technologies for developing the algorithms give the readers the possibility and not only, to choose the proper modules for their own research or business direction. The cryptographic modules contain algorithms based on machine learning and modern cryptographic algorithms: AES (Advanced Encryption System), SHA-256, RC4, RC5, RC6, MARS, BLOWFISH, TWOFISH, THREEFISH, RSA (Rivest-Shamir-Adleman), Elliptic Curve, and Diffie Hellman. As methods for implementing with success the cryptographic modules, we will propose a methodology which can be used as a how-to guide. The article will focus only on the first category, machine learning, and data clustering, algorithms with applicability in the cloud computing environment. For tests we have used a virtual machine (Virtual Box) with Apache Hadoop and a Biometric Analysis Tool. The weakness of the algorithms and methods implemented within the framework will be evaluated and presented in order for the reader to acknowledge the latest status of the security analysis and the vulnerabilities founded in the mentioned algorithms. Another important result of the authors consists in creating a scheme for biometric enrollment (in Results). The purpose of the scheme is to give a big overview on how to use it, step by step, in real life, and how to use the algorithms. In the end, as a conclusion, the current work paper gives a comprehensive background on the most important and challenging aspects on how to design and implement an authentication system based on biometrics characteristics.
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Khalid Taybi and Chaouki Chouraik. "Transforming cybersecurity in Moroccan banking: Implementing a smart system with machine learning and biometric recognition." Computer Science & IT Research Journal 6, no. 3 (2025): 231–42. https://doi.org/10.51594/csitrj.v6i3.1889.

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In the current era of digital transformation, the banking sector is undergoing significant changes, particularly within the Moroccan banking system, where data security has emerged as a critical concern. This paper investigates the pressing challenges posed by cyber threats, particularly the vulnerabilities associated with cloud-based data storage and online transactions. As incidents of cybercrime continue to escalate, there is an urgent need for effective intruder detection mechanisms to protect sensitive customer information. To address these challenges, this study proposes a Smart Online Banking System (SOBS) that integrates machine learning techniques with biometric recognition methods, including fingerprint scanning and facial recognition. By enhancing security protocols through advanced technologies, this model aims to mitigate risks associated with unauthorized access and bolster customer confidence in online banking services. The findings highlight the necessity for Moroccan banks to adopt innovative cybersecurity strategies that align with global best practices while addressing local challenges. Keywords: Cybersecurity, Moroccan Banking System, Machine Learning, Biometric Recognition, Cloud Data Security.
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Maaref, Zineb, Foudil Belhadj, Abdelouahab Attia, et al. "A comprehensive review of vulnerabilities and attack strategies in cancelable biometric systems." Egyptian Informatics Journal 27 (September 2024): 100511. http://dx.doi.org/10.1016/j.eij.2024.100511.

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Sindhujasri, Kode Lakshmi Durga, Kaduputla Manogna, Sutrayeth Hari Yuktha Nanda, Baligiri Thandava Krishna, and Attru Hanumantharao. "Decentralized and Biometric-Authenticated E-Voting System: A Blockchain-Based Approach." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1785–94. https://doi.org/10.22214/ijraset.2025.67653.

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Abstract: Elections play a fundamental role in any democratic system, and ensuring their integrity is of utmost importance. Traditional voting methods, such as paper ballots and Electronic Voting Machines (EVMs), suffer from various limitations, including security vulnerabilities, vote tampering, low voter turnout, delays in result processing, and a lack of transparency. Digital voting solutions offer convenience but raise concerns regarding data security and susceptibility to cyber threats. Blockchain technology presents a promising solution to these challenges by providing a decentralized, transparent, and tamperproof framework for conducting elections. As a distributed ledger system, blockchain records transactions in an immutable and verifiable manner, ensuring the integrity of votes. Key features such as decentralization, cryptographic security, transparency, and anonymity make blockchain a robust choice for secure e-voting. In this paper, we propose and implement a blockchainbased e-voting system using Ethereum smart contracts and Web3.js. Our system enforces single-use voting credentials, preventing duplicate votes, and leverages gas fees to mitigate fraudulent voting attempts. Additionally, we develop a web-based application that demonstrates the practical implementation of blockchain voting, discussing its advantages, challenges, and limitations in real-world scenarios
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Dwivedi, Abhishek, and Shekhar Verma. "SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept." Scientific Temper 13, no. 02 (2022): 165–72. http://dx.doi.org/10.58414/scientifictemper.2022.13.2.25.

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Face spoofing refers to “tricking” a facial recognition system to gain unauthorized access to aparticular system. It is mostly used to steal data and money or spread malware. The maliciousimpersonation of oneself is a critical component of face spoofing to gain access to a system.It is observed in many identity theft cases, particularly in the financial sector. In 2015, Wen etal. presented experimental results for cutting-edge commercial off-the-shelf face recognitionsystems. These demonstrated the probability of fake face images being accepted as genuine.The probability could be as high as 70%. Despite this, the vulnerabilities of face recognitionsystems to attacks were frequently overlooked. The Presentation Attack Detection (PAD)method that determines whether the source of a biometric sample is a live person or a fakerepresentation is known as Liveness Detection. Algorithms are used to accomplish this byanalyzing biometric sensor data for the determination of the authenticity of a source.
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Noh, Seungil, Jaehan Kim, Seokmin Lee, Youngshin Kang, Cheolsoo Park, and Youngjoo Shin. "Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes." Security and Communication Networks 2022 (September 29, 2022): 1–14. http://dx.doi.org/10.1155/2022/7997509.

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Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.
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Shinde, Trupti, Tanuja Nandre, and Gaurav Wable. "Enhanced Security for ATM Machines Using Facial Recognition and OTP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–3. http://dx.doi.org/10.55041/ijsrem37351.

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In recent years, the increasing sophistication of cyberattacks has exposed significant vulnerabilities in traditional ATM security systems, primarily those relying on Personal Identification Number (PIN)-based authentication. This paper presents an advanced security framework that combines biometric facial recognition with a One-Time Password (OTP) system to create a dual-factor authentication mechanism aimed at fortifying ATM security. The integration of these technologies addresses the shortcomings of conventional PIN-based systems, offering a robust solution to prevent unauthorized access and reduce fraud. The proposed system utilizes machine learning algorithms for facial recognition, ensuring reliable and accurate user identification under various conditions. Complementing this is an OTP sent to the user's registered mobile device, which adds a secondary layer of verification. The results from the implementation show a significant decrease in unauthorized access attempts and fraudulent activities, thereby enhancing the overall security of ATM transactions without compromising on user convenience. Keywords: ATM security, facial recognition, OTP, biometric authentication, dual-factor authentication, fraud prevention
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Khade, Smita, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha, Shilpa Gite, and Sudeep D. Thepade. "Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions." Inventions 6, no. 4 (2021): 65. http://dx.doi.org/10.3390/inventions6040065.

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Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.
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Loganathan, D. "Graphical Password Authentication: Image Grid Based Digital Lock for Mobile Apps." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4735–47. http://dx.doi.org/10.22214/ijraset.2023.52681.

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Abstract: Over the past few decades, research on authentication methods has advanced significantly. While the initial focus was on using standardised password selection and management approaches to secure textual passwords, this was made possible by learning about the flaws in the existing systems based on the attacks performed on the same systems' vulnerabilities. With the rise of biometric-based authentication, the next stage in the study and development of improved solutions has begun. Although initially rather entertaining, this authentication method is very insecure. Once a person's biometrics have been stolen or replicated, new biometrics cannot be made. Compared to a person's one-step biometric authentication, biometrics in the multifactor authentication of an enterprise are less vulnerable. The development of graphical passwords is the result of all these issues with authentication systems. While multifactor graphical passwords impede smartphone users' usability and speedier processing-computational flexibility, overly simplistic graphical passwords are prone to shoulder surfing. Users of Android devices need an authentication system that is easy to use, quick, secure, and that gains faster access to the device. A study was done evaluating the usability, predictability, resistance to various attacks, and other characteristics of various graphical passwords. Grid-based graphical passwords outperformed their rivals among the different types of graphical passwords that were taken into consideration. The majority of graphical password implementations in the past have been on personal computers (PCs), while smartphone usage is far more prevalent than on PCs. We propose to develop an Android application with gridbased image-based graphical password authentication that will be secure and help a lot of users protect the secondary identities that they carry about in the form of smartphones. After evaluating which factors must remain in the system, the application was divided into five modules. Each module was first built using flowcharts, and later, the Java programming language was used to build the app in Android Studio.
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Rupali, Baliram Navalkar, and Rajeshri R. Shelke Prof. "Design Pattern Classifiers under Attack for Security Evaluation using Multimodal System." International Journal of Trend in Scientific Research and Development 1, no. 4 (2017): 260–65. https://doi.org/10.31142/ijtsrd97.

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Pattern classification is a branch of machine learning that focuses on recognition of patterns and regularities in data. This Pattern classification system are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings. Here, propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier&#39;s behaviour in adversarial environments, and lead to better design choices. This framework can be applied to different classifiers on one of the application from the spam filtering, biometric authentication and network intrusion detection. So in this propose an algorithm for the generation of training and testing sets to be used for security evaluation. Now result shows providing security to system using application as blogger for this applying spam filtering, biometric authentication methods. That shows pattern classification for detecting spam comments which easy to detect spam. Rupali Baliram Navalkar | Prof. Rajeshri R. Shelke &quot;Design Pattern Classifiers under Attack for Security Evaluation using Multimodal System&quot; Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: https://www.ijtsrd.com/papers/ijtsrd97.pdf
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Wambua, Jacqueline Koki, and Dr Justice Mutua. "Biometric Strategies and Performance of Electronic Claim Processing at National Hospital Insurance Fund in Kenya." East African Scholars Journal of Economics, Business and Management 7, no. 06 (2024): 213–25. http://dx.doi.org/10.36349/easjebm.2024.v07i06.002.

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Over the years, there have been concerns about the rising number of fraudsters who work with hospitals to defraud businesses of millions of shillings in fake surgeries and treatments, while health service providers overcharge those who have insurance. To address this issue, medical insurance companies and businesses have implemented biometric authentication systems in collaboration with healthcare providers. The purpose of this study was to determine the effect of biometric adoption strategies on the performance of E-claim processing at NHIF Kenya. The research was guided by four theories that include Technology Acceptance theory, Technology Diffusion theory, and Institutional theory and Performance theory. This study used a descriptive research technique and the target population comprised of 78 senior executives at the NHIF offices in Upper Hill, Nairobi County. A census of 78 respondents was used for the study. The study established that NHIF Kenya had adopted various biometric strategies that included fingerprint recognition systems, Iris recognition strategy and facial recognition. The study concludes that clients expressed high preference with biometric data as it offered high level of security and quick access. NHIF Kenya had over the years realized an increase in operations efficiency, revenue from premium payment and quality delivery of services. It was recommended that NHIF should identify pain points, security vulnerabilities and areas where biometric authentication can be beneficial. The NHIF Kenya should continually evaluate effectiveness of E-claim processing system and should implement advanced fraud detection algorithms.
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Shah, Chirag Vinalbhai. "Securing Digital Transactions: The Role of AI, Big Data, and Biometric Authentication in Modern Payment Systems." Global Research and Development Journals 9, no. 7 (2024): 15–24. http://dx.doi.org/10.70179/grdjev09i100009.

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In an era where digital transactions are increasingly integral to economic activity, ensuring their security has become paramount. This paper explores the pivotal role of Artificial Intelligence (AI), Big Data, and Biometric Authentication in fortifying modern payment systems against fraud and cyber threats. AI algorithms enhance transaction security through predictive analytics and anomaly detection, while Big Data provides comprehensive insights into transactional patterns and potential vulnerabilities. Biometric authentication, incorporating technologies such as fingerprint recognition, facial recognition, and iris scanning, offers a robust layer of user verification that significantly reduces the risk of unauthorized access. By integrating these advanced technologies, payment systems can achieve a higher level of security, providing both consumers and financial institutions with greater confidence. This study discusses the current advancements in these fields, evaluates their effectiveness in real-world applications, and addresses the challenges and future directions for their integration in securing digital transactions.
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Pecolt, Sebastian, Andrzej Błażejewski, Tomasz Królikowski, Igor Maciejewski, Kacper Gierula, and Sebastian Glowinski. "Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems." Applied Sciences 15, no. 2 (2025): 887. https://doi.org/10.3390/app15020887.

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Facial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday devices. This study focuses on the development and validation of a facial recognition system utilizing a Haar cascade classifier and the AdaBoost machine learning algorithm. The system leverages characteristic facial features—distinct, measurable attributes used to identify and differentiate faces within images. A biometric facial recognition system was implemented on a Raspberry Pi microcomputer, capable of detecting and identifying faces using a self-contained reference image database. Verification involved selecting the similarity threshold, a critical factor influencing the balance between accuracy, security, and user experience in biometric systems. Testing under various environmental conditions, facial expressions, and user demographics confirmed the system’s accuracy and efficiency, achieving an average recognition time of 10.5 s under different lighting conditions, such as daylight, artificial light, and low-light scenarios. It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. Threshold optimization tests with six male participants revealed a value that effectively balances accuracy and efficiency. While the system performed effectively under controlled conditions, challenges such as biometric similarities and vulnerabilities to spoofing with printed photos underscore the need for additional security measures, such as thermal imaging. Potential applications include access control, surveillance, and statistical data collection, highlighting the system’s versatility and relevance.
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Khranovskyi, Mykola, and Andriy Kernytskyy. "ADVANTAGES OF USING LOCALITY-SENSITIVE HASHING FOR FINGERPRINT VERIFICATION IN ZERO-KNOWLEDGE PROTOCOLS." Computer Design Systems. Theory and Practice 7, no. 1 (2025): 64–72. https://doi.org/10.23939/cds2025.01.064.

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Biometric authentication offers a secure and convenient way to verify user identity, but traditional systems often require storing sensitive biometric templates, posing significant privacy risks. This paper explores the use of Locality-Sensitive Hashing (LSH) combined with zk-protocols to enable privacy-preserving fingerprint authentication without storing or exposing raw biometric data. LSH is particularly well-suited for fingerprint verification, as it allows similar feature vectors to map to the same or nearby hash buckets, accommodating natural variations in fingerprint scans. Unlike cryptographic hash functions such as SHA-256, LSH preserves similarity, ensuring that minor differences in scans do not prevent successful authentication. However, LSH is not a cryptographically secure function, and its susceptibility to hash collisions raises concerns about false acceptance rates (FAR). Our analysis demonstrates that, with a properly configured system–including an appropriate number of hash functions and buckets–the FAR can be reduced to negligible levels, making unauthorized authentication highly improbable. Furthermore, we address potential vulnerabilities, including whether LSH hashes can be inverted to recover the original biometric data. The results confirm that LSH is inherently non-invertible, preventing reconstruction of the original fingerprint. The integration of zk-protocols ensures that even LSH hashes do not need to be revealed during authentication, providing an additional layer of security. By proving knowledge of a valid fingerprint hash without disclosing it, users can be authenticated while preserving complete privacy. This approach presents a scalable and privacy-focused solution for biometric authentication, eliminating the need for centralized storage of biometric templates. It significantly reduces the risk of data breaches, identity theft, and unauthorized access, making it a strong candidate for secure authentication in privacy-sensitive applications.
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R., Pradeep, N. R. Sunitha, and G. S. Thejas. "A Modern Mechanism for Formal Analysis of Biometric Authentication Security Protocol." International Journal of Computer Network and Information Security 15, no. 3 (2013): 15–29. http://dx.doi.org/10.5815/ijcnis.2023.03.02.

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A Biometric Authentication Security (BAS) protocol is a method by which a person's unique physiological or behavioral characteristics are used to verify their identity. These characteristics can include fingerprints, facial features, voice patterns, and more. Biometric authentication has become increasingly popular in recent years due to its convenience and perceived security benefits. However, ensuring that the BAS protocols are secure and cannot be easily compromised. . Developing a highly secure biometric authentication protocol is challenging, and proving its correctness is another challenge. In this work, we present a modern mechanism for formally analyzing biometric authentication security protocol by taking a Aadhaar Level-0 Iris-based Authentication Protocol as a use case. The mechanism uses formal methods to formally verify the security of the Aadhaar Level-0 Iris-based Authentication protocol, and is based on the widely-used BAN logic (Buruccu, Abadi, and Needham). Using Scyther model checker we analyze the existing biometric authentication protocol and have shown its effectiveness in identifying potential security vulnerabilities. The proposed mechanism is based on a set of security requirements that must be met for the protocol to be considered secure. These requirements include the need for the protocol to be resistant to replay attacks, man-in-the-middle attacks, and impersonation attacks. The mechanism also considers the possibility of an attacker obtaining the biometric data of a legitimate user.
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Joni, Sohel Ahmed, Rabiul Rahat, Nishat Tasnin, Partho Ghose, Md Ashraf Uddin, and John Ayoade. "Hybrid-Blockchain-Based Electronic Voting Machine System Embedded with Deepface, Sharding, and Post-Quantum Techniques." Blockchains 2, no. 4 (2024): 366–423. http://dx.doi.org/10.3390/blockchains2040017.

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The integrity of democratic processes relies on secure and reliable election systems, yet achieving this reliability is challenging. This paper introduces the Post-Quantum Secured Multiparty Computed Hierarchical Authoritative Consensus Blockchain (PQMPCHAC-Bchain), a novel e-voting system designed to overcome the limitations of current Biometric Electronic Voting Machine (EVM) systems, which suffer from trust issues due to closed-source designs, cyber vulnerabilities, and regulatory concerns. Our primary objective is to develop a robust, scalable, and secure e-voting framework that enhances transparency and trust in electoral outcomes. Key contributions include integrating hierarchical authorization and access control with a novel consensus mechanism for proper electoral governance. We implement blockchain sharding techniques to improve scalability and propose a multiparty computed token generation system to prevent fraudulent voting and secure voter privacy. Post-quantum cryptography is incorporated to safeguard against potential quantum computing threats, future-proofing the system. Additionally, we enhance authentication through a deep learning-based face verification model for biometric validation. Our performance analysis indicates that the PQMPCHAC-Bchain e-voting system offers a promising solution for secure elections. By addressing critical aspects of security, scalability, and trust, our proposed system aims to advance the field of electronic voting. This research contributes to ongoing efforts to strengthen the integrity of democratic processes through technological innovation.
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Ramakrishnan, Mr R., B. Beula grace, and K. Shanmugapriya. "Civis ID with True Face: A Biometric Voting System for Secure and Transparent Elections." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–6. https://doi.org/10.55041/isjem02169.

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The "Civis ID with True Face" project presents an innovative approach to modernizing the electoral process by integrating biometric facial recognition technology for voter authentication. This system addresses critical challenges in traditional voting methods, such as voter fraud, impersonation, and inefficiencies caused by manual verification processes. By leveraging machine learning algorithms and advanced AI models, it ensures that only eligible voters can cast their votes, thus enhancing the security and integrity of elections. The project also prioritizes user accessibility, offering a seamless and contactless voting experience that reduces wait times and improves inclusivity for voters with disabilities or limited mobility. Key features of the system include real-time facial recognition for authentication, secure data encryption for voter privacy, and automated result tabulation for efficiency and accuracy. Designed to comply with international data protection standards, "Civis ID with True Face" not only eliminates traditional vulnerabilities in voting systems but also ensures transparency through secure and auditable digital records. This project represents a significant step toward embracing technology to enhance the trustworthiness, efficiency, and inclusivity of democratic processes worldwide.
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Abusham, Eimad, Basil Ibrahim, Kashif Zia, and Sanad Al Maskari. "An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System." Scientific Programming 2022 (November 25, 2022): 1–17. http://dx.doi.org/10.1155/2022/2628885.

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Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (&gt;40), and GDD values exceeded (0.92). Key space is determined by u 2 s i z e A 0 where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.
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Savkova, Tatiana, Ivan Opirskyy, and Dmytro Sabodashko. "STUDYING THE RESISTANCE OF BIOMETRIC AUTHENTICATION SYSTEMS TO ATTACKS USING VOICE CLONING TECHNOLOGY BASED ON DEEP NEURAL NETWORKS." Cybersecurity: Education, Science, Technique 2, no. 26 (2024): 27–43. https://doi.org/10.28925/2663-4023.2024.26.670.

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With the development of voice synthesis technologies based on deep neural networks, the security threats to biometric authentication systems that use voice recognition have increased. These systems, which were considered reliable, can be easily compromised by fake voices created using advanced models such as WaveNet, Tacotron 2, and other modern algorithms. In today's cybersecurity environment, such attacks jeopardize the confidentiality of personal data, which necessitates the improvement of protection methods. The purpose of this article is to study the resilience of biometric authentication systems to attacks using voice cloning technology, to analyze the effectiveness of modern synthesis methods for circumventing such systems, and to provide a comparative overview of various approaches to protect voice biometric data. The article discusses technologies that allow for the creation of accurate and realistic synthetic voices, as well as methods for detecting and protecting against fake signals. The article also analyzes the current vulnerabilities of voice systems and suggests strategies to increase resistance to such attacks, providing users with greater security and privacy.
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43

Kumar, Pratyush. "Secure Three Level Authentication System." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 6156–63. https://doi.org/10.22214/ijraset.2025.69782.

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This paper presents the design, development, and evaluation of a three-level authentication system. The increasing prevalence of cybercrimes has heightened the need for secure and efficient authentication systems to safeguard sensitive data. Traditional authentication mechanisms such as single factor or two factor systems, relying on text-based passwords, tokens, or biometric data, have been found to have vulnerabilities. This paper introduces a robust and user-friendly Secure Three Level Authentication System that combines text-based passwords, colour pattern recognition, and image-based authentication. By integrating these three methods, the system ensures a multilayered defines against common security threats such as phishing, brute force attacks, and shoulder surfing. The first layer utilizes a passphrase-based text password, designed for ease of use while maintaining complexity. The second layer involves a graphical password using RGB colour patterns, leveraging visual memory for added security. Finally, the third layer employs image-based authentication, where users segment and rearrange a chosen image for secure access. The system is implemented using PYTHON, CSS, and HTML, ensuring a seamless and efficient user experience. Designed with the waterfall model, the authentication process involves registration and login phases, where each layer must be passed sequentially for access. This three-level system addresses the vulnerabilities of conventional methods by increasing password difficulty at each stage. While slightly more time-consuming, it offers significant advantages for applications requiring high security standards, such as corporate environments, sensitive data repositories, and critical infrastructures. Future iterations aim to enhance the system's adaptability and user customization. The proposed system represents a significant advancement in authentication technology, providing a balance between usability and security to protect against evolving cyber threats.
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Andrian, Rafi, and Gede Putra Kusuma. "Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1260. http://dx.doi.org/10.62527/joiv.8.3.2247.

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Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements
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45

Haq, Muhamad Amirul, Le Nam Quoc Huy, Muhammad Ridlwan, and Ishmatun Naila. "Multi-Angle Facial Recognition: Enhancing Biometric Security with a Broadly Positioned Stereo-Camera System." E3S Web of Conferences 500 (2024): 03032. http://dx.doi.org/10.1051/e3sconf/202450003032.

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This study addresses the vulnerabilities of traditional monocular camera-based face recognition systems, emphasizing the need for improved security and reliability in biometric authentication under varying environmental conditions, lighting, and human poses. To counteract the risk of spoofing attacks using masks or static images, we introduce a multi-angle stereo camera system. This system is strategically designed to capture facial imagery from multiple perspectives, thereby enhancing depth perception and spatial accuracy, crucial for high-security authentication. Employing a novel image processing approach, the study integrates a Convolutional Neural Networks (CNN) with a simple Boolean operation to differentiate the landmarks detected on each camera. This method exploits CNN’s robust feature extraction capabilities and the effective usage of stereo camera, enabling precise detection and analysis of 3D facial landmarks. Such an approach significantly bolsters the system’s ability to differentiate between genuine faces and deceptive representations like masks or static images. Empirical results demonstrate that the stereo camera configuration substantially improves recognition accuracy, reducing both false positives and negatives, especially in controlled spoofing scenarios. The advanced 3D facial landmark detection further reinforces the system’s security. With its enhanced robustness and security, the developed system shows great potential for applications in areas requiring stringent identity verification, such as banking, public facilities, and smart home technologies.
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46

Kang, Dongwoo, Jaewook Jung, Hyoungshick Kim, Youngsook Lee, and Dongho Won. "Efficient and Secure Biometric-Based User Authenticated Key Agreement Scheme with Anonymity." Security and Communication Networks 2018 (June 20, 2018): 1–14. http://dx.doi.org/10.1155/2018/9046064.

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At present, a number of users employ an authentication protocol so as to enjoy protected electronic transactions in wireless networks. In order to establish an efficient and robust the transaction system, numerous researches have been conducted relating to authentication protocols. Recently, Kaul and Awasthi presented an user authentication and key agreement scheme, arguing that their scheme is able to resist various types of attacks and preserve diverse security properties. However, this scheme possesses critical vulnerabilities. First, the scheme cannot prevent two kinds of attacks, including off-line password guessing attacks and user impersonation attacks. Second, user anonymity rule cannot be upheld. Third, session key can be compromised by an attacker. Fourth, there is high possibility that the time synchronization trouble occurs. Therefore, we suggest an upgraded version of the user authenticated key agreement method that provides enhanced security. Our security and performance analysis shows that compared, to other associated protocols, our method not only improves the security level but also ensures efficiency.
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47

Carrillo-Torres, Diego, Jesús Arturo Pérez-Díaz, Jose Antonio Cantoral-Ceballos, and Cesar Vargas-Rosales. "A Novel Multi-Factor Authentication Algorithm Based on Image Recognition and User Established Relations." Applied Sciences 13, no. 3 (2023): 1374. http://dx.doi.org/10.3390/app13031374.

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Conventional authentication methods, like simple text-based passwords, have shown vulnerabilities to different types of security attacks. Indeed, 61% of all breaches involve credentials, whether stolen via social engineering or hacked using brute force. Therefore, a robust user authentication mechanism is crucial to have secure systems. Combining textual passwords with graphical passwords in a multi-factor approach can be an effective strategy. Advanced authentication systems, such as biometrics, are secure, but require additional infrastructure for efficient implementation. This paper proposes a Multi-Factor Authentication (MFA) based on a non-biometric mechanism that does not require additional hardware. The novelty of the proposed mechanism lies in a two-factor authentication algorithm which requires a user to identify specific images out of a set of randomly selected images, then the user is required to establish a self-pre-configured relation between two given images to complete authentication. A functional prototype of the proposed system was developed and deployed. The proposed system was tested by users of different backgrounds achieving 100% accuracy in identifying and authenticating users, if authentication elements and credentials were not forgotten. It was also found to be accepted by the users as being easy to use and preferable over common MFA mechanisms.
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48

Vinutha, H., and G. Thippeswamy. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. https://doi.org/10.11591/csit.v4i1.pp1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community&#39;s interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
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49

Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establishes a robust authentication system, countering vulnerabilities in unimodal methods. This approach leverages ECG's robustness against spoofing attacks and lip's fine-grained behavioral cues for comprehensive authentication. Ensemble learning techniques, from majority voting to advanced methods, harness the strengths of individual models, improving accuracy, reliability, and generalization. Moreover, ensemble learning detects anomalies, enhancing security. The study incorporates ECG signal filtering and lip region extraction as preprocessing, uses wavelet transform for ECG features, SIFT for lip image features, and employs greywolf optimization for feature selection. Ultimately, a voting-based ensemble classifier is applied for classification, showcasing the potential of this integrated approach in fortified security measures.
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50

Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524–35. https://doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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Abstract:
In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establishes a robust authentication system, countering vulnerabilities in unimodal methods. This approach leverages ECG's robustness against spoofing attacks and lip's fine-grained behavioral cues for comprehensive authentication. Ensemble learning techniques, from majority voting to advanced methods, harness the strengths of individual models, improving accuracy, reliability, and generalization. Moreover, ensemble learning detects anomalies, enhancing security. The study incorporates ECG signal filtering and lip region extraction as preprocessing, uses wavelet transform for ECG features, SIFT for lip image features, and employs greywolf optimization for feature selection. Ultimately, a voting-based ensemble classifier is applied for classification, showcasing the potential of this integrated approach in fortified security measures.
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