Biometric Authentication Methods Research Articles

Presentation Attack Detection: A Systematic Literature Review

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

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

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

Perancangan Sistem Keamanan Rumah Menggunakan Face Recognition (Pengenalan Wajah) Berbasis Raspberry Pi

Talking about technology, currently computer technology is developing very rapidly and is a field that has a very important role in several aspects of human life, including in the field of security. Currently, many home or room security systems have been developed with several identity verification methods using a computer system, using keys, cards, passwords, and so on. However, this method still has shortcomings such as humans' limitations in remembering number combinations which makes the door inaccessible. Therefore, techniques for reliable and accurate identification or verification can be designed using biometric technology that utilizes the special characteristics of the human individual.

Delusio - Plausible Deniability For Face Recognition

We developed an Android phone unlock mechanism utilizing facial recognition and specific mimics to access a specially secured portion of the device, designed for plausible deniability. The widespread adoption of biometric authentication methods, such as fingerprint and facial recognition, has revolutionized mobile device security, offering enhanced protection against shoulder-surfing attacks and improving user convenience compared to traditional passwords. However, a downside is the potential for third-party coercion to unlock the device. While text-based authentication allows users to reveal a hidden system by entering a special password, this is challenging with face authentication. We evaluated our approach in a role-playing user study involving 50 participants, with one participant acting as the attacker and the other as the suspect. Suspects successfully accessed the secured area, mostly without detection. They further expressed interest in this feature on their personal phones. We also discuss open challenges and opportunities in implementing such authentication mechanisms.

Iris Identification Based on SVM-CNN Method

A person is automatically recognized in a biometric system by analyzing their distinct traits. Most people agree that iris recognition is the most accurate and dependable biometric identification method currently in use. The goal is to accurately and efficiently identify a person in real time by analyzing the sporadic patterns seen in the iris if an eye from some distance, by extracting strong features using deep learning technique. The extracting of significant features is important step that effect the overall accuracy of iris recognition system. In order to extract the iris characteristics, this research suggests a robust convolution neural network (CNN) structure. Then, an identity of the person is determine based on extracted features from his iris to support vector machine (SVM). The proposed system is examined on CASIA V. (1). Several parameters such as accuracy, precision, recall, and F-score are computed to evaluate the performance of the proposed system. The obtained result of the accuracy is about (99%). The proposed system results are compared with several previous methods and prove its effectiveness. A contemporary approach and the suggested method are contrasted and use deep learning for features extraction and the results depict our method outperforms other methods.

Deep Learning Based Offline Handwritten Signature Recognition

In our digitalized world, the need for reliable authentication methods is steadily increasing. Biometric authentication methods are divided into two main categories: physiological and behavioral. While physiological biometrics include features such as face, iris, and fingerprint, behavioral biometrics encompass dynamics such as gait, speech, and signature. Most of these methods require specialized equipment, whereas signatures can be easily obtained without additional tools, making them ideal for verifying the legality of documents. Although manual signature recognition is effective, it is resource-intensive, slow, and susceptible to errors. With advancements in technology, the need to automate the signature recognition process to enhance accuracy and efficiency has become increasingly important. Based on this need, in this study, five different DL techniques (GoogLeNet, MobileNet-V3 Large, Inception-V3, ResNet50 and EfficientNet-B0) are used to classify signature images with detailed analyses. DL methods have outperformed traditional techniques by leveraging the power of CNNs to automatically learn and extract complex features from signature data. The dataset used consists of a total of 12,600 images belonging to 420 individuals, each contributing 30 original signatures. The dataset is divided into training, validation, and test sets in different proportions to analyze classification performance. The pre-trained DL models were fine-tuned to optimize their parameters for the signature dataset. The results demonstrate that DL models achieve high accuracy in signature classification, with the GoogLeNet and Inception-V3 models reaching an accuracy of 98.77% at a 20% test rate. The study also highlights the impact of different test rates on model performance.

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.

Realization of Reliable and Effective Authentication in Intelligent Systems by Using Visual Biometrics Methods

The main purpose of this article is to consider the aspects of ensuring security and increasing the efficiency of the authentication process in intelligent systems using visual biometrics. The work is aimed at the development and improvement of authentication systems using advanced biometric identification methods. An intelligent system has been created that ensures secure authentication of users of the current system, using a Siamese neural network. In addition to the implementation of basic security measures in the form of hashing and saving user logins and passwords, the implementation of two-factor authentication is important nowadays, which significantly strengthens the protection of user data and prevents most modern methods of hacking and stealing user data. Two-factor authentication is implemented as a technology for searching, recognizing and comparing the faces of system users, as visual biometrics is more secure than other types of two-factor authentication. Different variations of the possible implementation of Siamese neural network using Contrastive loss function and more modern Triplet loss function were reviewed and accordingly, a neural network using Triplet loss function was accomplished and trained. After training and verifying the correct operation of the neural network, it was integrated into the created intelligent system, thanks to which an effective way of recognizing the face of the system user was created, saving the received information in the database and further comparing the current user with the stored face during authentication. As a result, a secure and reliable intelligent system was created that cutting down the risk of unapproved access to the user account and uses an effective and modern method of user authentication.

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.

Emerging trends in gait recognition based on deep learning: a survey.

Gait recognition, a biometric identification method, has garnered significant attention due to its unique attributes, including non-invasiveness, long-distance capture, and resistance to impersonation. Gait recognition has undergone a revolution driven by the remarkable capacity of deep learning to extract complicated features from data. An overview of the current developments in deep learning-based gait identification methods is provided in this work. We explore and analyze the development of gait recognition and highlight its uses in forensics, security, and criminal investigations. The article delves into the challenges associated with gait recognition, such as variations in walking conditions, viewing angles, and clothing as well. We discuss about the effectiveness of deep neural networks in addressing these challenges by providing a comprehensive analysis of state-of-the-art architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. Diverse neural network-based gait recognition models, such as Gate Controlled and Shared Attention ICDNet (GA-ICDNet), Multi-Scale Temporal Feature Extractor (MSTFE), GaitNet, and various CNN-based approaches, demonstrate impressive accuracy across different walking conditions, showcasing the effectiveness of these models in capturing unique gait patterns. GaitNet achieved an exceptional identification accuracy of 99.7%, whereas GA-ICDNet showed high precision with an equal error rate of 0.67% in verification tasks. GaitGraph (ResGCN+2D CNN) achieved rank-1 accuracies ranging from 66.3% to 87.7%, whereas a Fully Connected Network with Koopman Operator achieved an average rank-1 accuracy of 74.7% for OU-MVLP across various conditions. However, GCPFP (GCN with Graph Convolution-Based Part Feature Polling) utilizing graph convolutional network (GCN) and GaitSet achieves the lowest average rank-1 accuracy of 62.4% for CASIA-B, while MFINet (Multiple Factor Inference Network) exhibits the lowest accuracy range of 11.72% to 19.32% under clothing variation conditions on CASIA-B. In addition to an across-the-board analysis of recent breakthroughs in gait recognition, the scope for potential future research direction is also assessed.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

РОЗВИТОК МОБІЛЬНИХ ПЛАТЕЖНИХ СЕРВІСІВ: МАРКЕТИНГ FINTECH ПРОДУКТІВ

In today's world, financial technology (FinTech) has become an integral part of everyday life, significantly changing the way people interact with financial services. One of the most dynamically developing industries is mobile payment services. They provide a fast, convenient and secure way to carry out financial transactions through mobile devices. However, despite the rapid spread of mobile payment services, there are a number of problems and challenges associated with their development and promotion on the market. The purpose of the study is to analyze the development of mobile payment services in the context of FinTech, as well as to develop effective marketing strategies for promoting these products on the market. The object of the study is mobile payment services as an innovative segment of FinTech and their marketing in the modern market, namely aspects related to the development, implementation, promotion and use of mobile payment services. Scientific publications and monographic editions, magazine articles and materials of scientific and practical conferences became the methodological basis of the research. Content analysis of scientific periodicals was used during the research; comparative critical analysis of existing approaches and methods of analysis of financial stability and tax burden; statistical methods of analysis. The main hypothesis of the research is that effective marketing strategies focused on technological innovation, personalization of user experience and taking into account the cultural characteristics of markets significantly increase the adoption and use of mobile payment services among consumers. Summary of the main material. Innovative technologies and platforms based on the concept of embedded finance and their potential to change and transform the financial sphere are highlighted. The technological, economic and regulatory aspects of the use of embedded finance are analyzed, and the key challenges and opportunities associated with their application are identified. As a result of the study, it is established that integrated finance can become a significant factor in accelerating the innovative development of the financial sector, improving the quality of financial services, and ensuring greater accessibility and efficiency in the provision of financial products and services. Originality and practical significance. The originality of the scientific article lies in the comprehensive approach to the study of mobile payment services through the prism of marketing strategies in the FinTech sector. Despite the considerable interest in the technological aspects of mobile payments, the issues of effective marketing of these products remain less studied. The practical significance of the article is that the research results can be used by both FinTech companies and marketing specialists to improve their strategies and increase the efficiency of mobile payment services. Conclusions and prospects for further research. The analysis showed that the introduction of the latest technologies, such as blockchain, artificial intelligence and biometric authentication methods, significantly increases the security and convenience of mobile payment services, which contributes to their popularization among consumers. The combination of technological innovation, personalization and cultural adaptation in marketing strategies is the key to successful promotion of mobile payment services. Further research is needed to assess the long-term consequences of introducing new technologies into mobile payment services. Particular attention should be paid to data security and confidentiality.

Decentralized Identity Verification in Metaverse Auditing Using Blockchain Technology

This research paper presents a comprehensive review of decentralized identity verification in the context of metaverse auditing, leveraging blockchain technology. With the rapid evolution of virtual environments and the emergence of the metaverse, ensuring trust, transparency, and security becomes paramount. Traditional centralized identity verification systems face challenges in such decentralized and dynamic environments. Blockchain technology offers promising solutions by providing a transparent, immutable, and decentralized ledger for identity management and verification. Conceptual Analysis research methodology used in this research paper because theoretical and conceptual clarity are crucial for understanding complex phenomena. The paper begins by exploring the concept of decentralized identity management systems and their significance in virtual environments. It discusses various challenges associated with traditional identity verification methods and highlights the potential of blockchain-based solutions in addressing these challenges. Through a systematic literature review, the paper examines existing research and developments in decentralized identity verification, metaverse auditing, and blockchain technology. Key topics covered include the architecture of decentralized identity systems, consensus mechanisms for identity verification on blockchain networks, privacy-preserving techniques, and interoperability standards for cross-platform identity management. Furthermore, the paper investigates the role of smart contracts and decentralized applications (DApps) in facilitating identity verification processes within the metaverse. The review also discusses the potential implications of decentralized identity verification in enhancing trust and security in virtual economies, enabling seamless user experiences, and fostering innovation in digital asset management. Moreover, it identifies current gaps and challenges in the field and proposes future research directions to address these issues. Overall, this research paper contributes to the understanding of decentralized identity verification in metaverse auditing using blockchain technology. It serves as a valuable resource for researchers, practitioners, and policymakers interested in exploring the intersection of decentralized identity, virtual environments, and blockchain technology. The insights provided aim to stimulate further research and innovation in this rapidly evolving domain.

An Enhanced Human Gait Recognition System using UALNN Classifier

Human gait recognition is a biometric identification method that uses a person’s gait patterns to identify them. Gait Recognition (GR) is the steps to follow in recognizing walking style of an individual. It identifies a person as of a longer distance with higher precision owing to its unique characteristics; thus, it is mostly utilized for tracking purposes. In computer vision software, specifically, to track the same individual across multiple scenarios, the individual recognition across numerous cameras is a significant task. However, owing to the position of caught image or video, human identification by means of GR could be limited. Therefore, for person re-identification, a novel GR methodology has been proposed here. Five phases were undergone by the proposed methodology. Initially, from an openly accessible dataset, the input video is collected, which is then transmuted into a number of frames. To eliminate the noise from the frames, the Mean Modified Filter (MMF) is utilized after frame conversion. The Gaussian Taxicab Mixture Model (GTMM) is employed to get rid of the background from the frames. After that, Similarity Histogram Silhouette Segmentation (SHSS) model is utilized to segment the humans’ silhouette as of the background removed frames; thus, generating the energy image. The features are extracted from this energy image. The Uniform Alex Neural Network classifier (UALNN) uses these features to identify the human gait. Improved recognition performance is attained from the experiential outcomes. The overall recognition outcomes obtained concluded that the proposed methodology is highly promising.

Person Authentication System Using Multimodal Biometrics

Biometrics is the automated process used to recognize human by measuring their behavioural and physiological characteristics. Biometrics are generally used either for verification The use of biometric for identification purposes requires that a particular biometric factor be unique for each individual that it can be calculated, and that it is invariant over time. Biometrics such as signatures, photographs, fingerprints, voiceprints and retinal blood vessel patterns all have noteworthy drawbacks. Although signatures and photographs are cheap and easy to obtain and store, they are impossible to identify automatically with assurance, and are easily forget. Human iris on the other hand as an internal organ of the eye and as well protected from the external environment, yet it is easily visible from within one meter of distance makes it a perfect biometric for an identification system with the ease of speed, reliability and automation Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on images of the irises of an individual’s eyes, whose complex random patterns are unique. Proposed system provides a comprehensive implementation of periocular biometrics and a deep insight of various aspects such as utility of peri-ocular region In this project face and eye points are captured using Grassmann algorithm and Gabor filter for eye features extraction. Each trait is analysed separately and given its own score. The results are combined using deep leaning algorithm to provide a single decision in real time environments.

Vulnerabilities in One Time Passwords and Protection Method Using Secret Key and Biometric Hash Code

In the present era, conducting financial transactions online is both simple and widely embraced for its convenience. However, engaging in internet-based financial transactions exposes individuals to potential threats like password attacks, malware, phishing, and other illicit activities. Recognizing these risks, many banks have enhanced security measures by incorporating One-Time Password (OTP) authentication alongside traditional login credentials. Despite these improvements, security issues persist in online transactions. To mitigate such concerns, OTPs can be delivered via SMS to the account owner's mobile number. Even with heightened security, internet banking remains susceptible to various attacks, including online phishing, Man-in-the-Middle attacks, SQL injection, and brute force attacks. To address these challenges, this proposed model introduces a Biometric mobile OTP-based transaction authentication method.

A framework for security improvement on usage of mobile money application based on iris biometric authentication method

ABSTRACT Offering transactions through mobile devices has many advantages, such as cashless payments, lower transaction costs, and provide employment opportunities. However, it introduces access security challenges that must be dealt with, which may allow unauthorized access, resulting in theft. This work proposes a framework to improve security on the usage of Mobile Money Services (MMS) by using two-factor authentication (2FA) of PIN and iris biometric authentication method (IRBAM). The rapid application development (RAD) approach was used to develop mobile money applications based on the proposed framework. The proposed framework will improve the security of accessing MMS.

Analysis of EEG Signals Between Motor Imaginary Tasks and Rest Condition for Biometric Application

Biometric technology has gained immense popularity as an effective solution for enhancing cybersecurity, specifically in countering financial fraud and security threats. EEG-based authentication is unique among biometric authentication methods due to its unparalleled confidentiality and non-replicability. This study explores the feasibility of using motor imagery tasks and rest conditions for human authentication. Ten physically fit subjects, aged between 20 and 28 years, participated voluntarily in the study. The subjects perform imaginary tasks involving their left and right-hand movement. Each task lasted for two minutes, separated by a one-minute break, and EEG data were collected using the EPOC+ device, which features 14-channel electrodes. The sampling frequency was set at 128 Hz. To extract relevant frequency information, Butterworth bandpass filters were employed to extract the alpha (8-13Hz), beta (14-30Hz), and gamma (30-42Hz) frequency bands. Linear features, such as power spectral density (PSD), were obtained using the Welch Method and the Burg Method, while spectral entropy was used to extract non-linear features. Statistical features mean, median, standard deviation, minimum, and maximum were derived from the PSD, and spectral entropy was used as input for the classifiers. Multiple classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), decision tree and Naive Bayes, were employed for the classification task. The Welch method combined with the support vector machine classifier achieved a higher classification accuracy of 96.83% for the beta waves from channels C3, C4, O1, and O2, corresponding to the frontal and occipital lobes. Interestingly, the rest conditions exhibited a higher classification accuracy of 96.83% compared to the motor-imagery tasks, which achieved 96.04%. The utilization of motor imagery tasks and rest conditions, along with the application of advanced classification techniques, holds promise for the development of robust and reliable biometric systems in cybersecurity.

Improving user security during a call

The recent development of mobile networks has led to the emergence of new threats and methods of implementing existing ones. Phishing attacks, including robocalls, are causing record losses to both individual users and large corporations. At the same time, existing countermeasures cannot provide protection against such attacks because most existing solutions focus on device authentication, whereas user authentication does not occur during a call. Another problem with mobile networks is that there is no point-to-point encryption, i.e., the speech is encrypted only on the segment from the subscriber to the base station. The subject of study in this article is the process of ensuring user security during a call. The purpose of this study is to develop a model of mutual user authentication and end-to-end data encryption in a mobile network during a call. The main objectives are the protection of users from spoofing and vishing and the proposal of a protection method by implementing mutual authentication of users during a call without storing confidential information on the side of a "trusted third party". Method of secure key exchange and end-to-end encryption during a call in the mobile network was proposed. It prevents the interception of calls by the operator for circuit-switched and packet networks. The methods used are mathematical modelling, ontological approach, and multi-criteria optimization models. Because of this research, an algorithm for mutual authentication of users is proposed by introducing biometric authentication methods and modifying the sequence of messages during a call. The proposed approach can be implemented for CS-call and VoLTE/VoWiFi calls. A call cannot be received without user biometric authentication; such as ear pattern or bone conduction methods. Modified SETUP and CONNECT ACK messages are used to inform the other party about the user verification result. This prevents user spoofing, call masquerading, and robocalls. A combination of the proposed asymmetric encryption, a short authentication string, and hashes of previous calls provides a higher level of confidentiality, integrity, and additional resistance to man-in-the-middle attacks. Conclusions. The scientific novelty of the obtained results is the integration of the above methods into the sequence of call flow messages for providing mutual authentication, end-to-end encryption, and counteraction to the number of network attacks. The proposed methods allow one level to increase the provision of services of privacy and observation groups and can be implemented in the software part of user equipment.

Analysis of Biometric-Based Cryptographic Key Exchange Protocols—BAKE and BRAKE

Biometric authentication methods offer high-quality mechanisms to confirm the identity of individuals in security systems commonly used in the modern world, such as physical access control, online banking, or mobile device unlocking. They also find their application in cryptographic solutions, which allow the biometrically authenticated exchange of cryptographic keys between users and services on the internet, despite the fuzziness of biometric data. Such solutions are BAKE (biometrics-authenticated key exchange) and BRAKE (biometric-resilient authenticated key exchange) protocols, upon which our work is based. However, the direct application of fuzzy biometrics in cryptography, which relies heavily on the accuracy of single-bit secret values, is not trivial. Therefore, this paper is devoted to analyzing the security of this idea and the feasibility of implementing biometric AKE (authenticated key exchange) protocols, with an emphasis on the BRAKE protocol. As the results of our analysis, we discuss BRAKE’s limitations and vulnerabilities, which need to be appropriately addressed to implement the protocol in modern systems.