Biometric Authentication Methods Research Articles

Employing Some Statistical Models in Facial Recognition

Face recognition is a computer vision approach that analyzes facial features to identify or verify a person's identity. it may be required to combine different biometric identification methods to improve security and recognition accuracy. To identify an individual, facial recognition systems simulate human face understanding and recognition abilities management, there is difficulty in detecting data when using various patterns of facial image capture, especially when there is a large overlap between the patterns, it takes time to determine a person's membership, Many statistical methods have been proposed in recent years, and in this study, will be employing the Support Vector Machine (SVM), which is a statistical model for face recognition that has been used for classification and regression tasks, so this model will be compared with logistic regression, one of the significant statistical models used in prediction, classification, and facial recognition. Various methods will be evaluated with a group of datasets and varied samples (number of images); this group represented the real data, which consisted of 100 individuals. The results showed that SVM had the lowest mean square error (MSE) for the dataset, followed by LR, and all the methods reached the highest accuracy rate of 100% for facial image recognition on real data.

Noise-Robust Biometric Authentication Using Infrared Periocular Images Captured from a Head-Mounted Display

This study proposes a biometric authentication method using infrared (IR)-based periocular images captured in virtual reality (VR) environments with head-mounted displays (HMDs). The widespread application of VR technology highlights the growing need for robust user authentication in immersive environments. To address this, the study introduces a novel periocular biometric authentication system optimized for HMD usage. Ensuring reliable authentication in VR environments necessitates overcoming significant challenges, including flicker noise and infrared reflection. Flicker noise, caused by alternating current (AC)-powered lighting, produces banding artifacts in images captured by rolling-shutter cameras, obstructing biometric feature extraction. Additionally, IR reflection generates strong light glare on the iris surface, degrading image quality and negatively impacting the model’s generalization performance and authentication accuracy. This study utilized the AffectiVR dataset, which includes noisy images, to address these challenges. In the preprocessing phase, iris reflections were removed, reducing the Equal Error Rate (EER) from 6.73% to 5.52%. Furthermore, incorporating a Squeeze-and-Excitation (SE) block to mitigate flicker noise and enhance model robustness resulted in a final EER of 6.39%. Although the SE block slightly increased the EER, it significantly improved the model’s ability to suppress noise and focus on critical periocular features, ensuring enhanced robustness in challenging VR environments. Heatmap analysis revealed that the proposed model effectively utilized periocular features, such as the skin around the eyes and eye contours, compared to prior approaches. This study establishes a crucial groundwork for advancing robust biometric authentication systems capable of overcoming noise challenges in next-generation immersive platforms.

ENHANCING SURVEILLANCE SECURITY: USING GENERATIVE ADVERSARIAL NETWORKS AND COMPUTER VISION TO PREVENT IDENTITY SPOOFING IN FACIAL RECOGNITION SYSTEMS

Facial recognition systems have become integral to modern security infrastructures, offering a reliable method for identity verification. However, these systems are vulnerable to spoofing attacks, where adversaries use images, videos, or 3D masks to impersonate individuals and bypass authentication. Traditional anti-spoofing methods often fail to detect sophisticated attacks, necessitating the development of more robust solutions. The primary objective of this research was to explore the potential of GANs in generating synthetic data to train advanced facial recognition models and improve their resistance to spoofing. The study aimed to evaluate the effectiveness of integrating GANs with computer vision techniques for detecting and mitigating spoofing attempts in real-time surveillance scenarios. The results indicate that GAN-based models significantly improve the ability of facial recognition systems to identify and reject spoofed identities. By generating adversarial examples, the GANs enhanced the training process, enabling the facial recognition system to learn subtle patterns indicative of spoofing, such as inconsistencies in facial texture or lighting. The integration of computer vision techniques further strengthened the model's performance, allowing it to detect spoofing attacks in various conditions, including different angles, lighting variations, and partial occlusions. The system demonstrated increased accuracy in distinguishing genuine identities from spoofed ones, with improved adaptability to real-world challenges. This study demonstrates that GANs, when combined with computer vision, offer a promising solution to enhance the security of facial recognition systems against identity spoofing. The approach improves the resilience of these systems in real-world applications by detecting more sophisticated spoofing techniques. However, limitations include the need for large, high-quality datasets to train the models effectively and the computational resources required for real-time processing.

Electrocardiogram-Based Driver Authentication Using Autocorrelation and Convolutional Neural Network Techniques

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system’s performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications.

KeyGAN: Synthetic keystroke data generation in the context of digital phenotyping

Objective:This paper aims to introduce and assess KeyGAN, a generative modeling-based keystroke data synthesizer. The synthesizer is designed to generate realistic synthetic keystroke data capturing the nuances of fine motor control and cognitive processes that govern finger-keyboard kinematics, thereby paving the way to support biomarker development for psychomotor impairment due to neurodegeneration. Methods:KeyGAN is designed with two primary objectives: (i) to ensure high realism in the synthetic distributions of the keystroke features and (ii) to analyze its ability to replicate the subtleties of natural typing for enhancing biomarker development. The quality of synthetic keystroke data produced by KeyGAN is evaluated against two keystroke-based applications, TypeNet and nQiMechPD, employed as’referee’ controls. The performance of KeyGAN is compared with a reference random Gaussian generator, testing its ability to fool the biometric authentication method TypeNet, and its ability to characterize fine motor impairment in Parkinson’s Disease using nQiMechPD. Results:KeyGAN outperformed the reference comparator in fooling the biometric authentication method TypeNet. It also exhibited a superior approximation to real data than the reference comparator when using nQiMechPD, showcasing its adaptability and versatility in mimicking early signs of Parkinson’s Disease in natural typing. KeyGAN’s synthetic data demonstrated that almost 20% of real PD samples could be replaced in the training set without a decline in classification performance on the real test set. Low Fréchet Distance (<0.03) and Kullback–Leibler Divergence (<700) between KeyGAN outputs and real data distributions underline the high performance of KeyGAN. Conclusion:KeyGAN presents strong potential as a realistic keystroke data synthesizer, displaying impressive capability to reproduce complex typing patterns relevant to biomarkers for neurological disorders, like Parkinson’s Disease. The ability of its synthetic data to effectively supplement real data for training algorithms without affecting performance implies significant promise for advancing research in digital biomarkers for neurodegenerative and psychomotor disorders.

Near-Field Communication (NFC) Cyber Threats and Mitigation Solutions in Payment Transactions: A Review.

Today, many businesses use near-field communications (NFC) payment solutions, which allow them to receive payments from customers quickly and smoothly. However, this technology comes with cyber security risks which must be analyzed and mitigated. This study explores the cyber risks associated with NFC transactions and examines strategies for mitigating these risks, focusing on payment devices. This paper provides an overview of NFC technology, related security vulnerabilities, privacy concerns, and fraudulent activities. It then investigates payment devices such as smartphones, contactless cards, and wearables, highlighting their features and vulnerabilities. The study also examines encryption, authentication, tokenization, biometric authentication, and fraud detection methods as risk mitigation strategies. The paper synthesizes theoretical frameworks to provide insights into NFC transaction security and offers stakeholder recommendations.

Integration of Sensory Memory Process Display System for Gait Recognition

AbstractGait is among the most dependable, accurate, and secure methods of biometric identification. However, high power consumption and low computing capability are two major obstacles on wearable sensors‐based gait recognition system. In this work, an integrated system is reported combining a triboelectric nanogenerator (TENG), a memristor (Ag/HfOx/Pt), and perovskite‐based multicolor LEDs (PMCLED) for the visualization and recognition of foot patterns through signal‐on‐none and multi‐wavelength on‐device preprocessing. The flexible TENG acts as a sensory receptor, generating voltage based on the duration and intensity of pressure, which in turn promotes voltage‐triggered synaptic plasticity in the memristor. The PMCLED, with its threshold switching and multi‐wavelength emission characteristics, enables nonlinear filtering and amplification of the synaptic signal from the memristor, resulting in a simplified system design and reduced background noise. Additionally, the effectiveness of on‐device preprocessing is validated based on a 5 × 5 array of integrated devices and software‐built neural network for foot pattern visualization and recognition. The proposed system is able to recognize the on‐device preprocessed images with high accuracy, indicating great potentials in both healthcare monitoring and human‐machine interaction.

OPTIMIZING AUTHENTICATION SECURITY IN INTELLIGENT SYSTEMS THROUGH VISUAL BIOMETRICS FOR ENHANCED EFFICIENCY

Context. The primary objective of this article is to explore aspects related to ensuring security and enhancing the efficiency of authentication processes in intelligent systems through the application of visual biometrics. The focus is on advancing and refining authentication systems by employing sophisticated biometric identification methods. Objective. A specialized intelligent system has been developed, utilizing a Siamese neural network to establish secure user authentication within the existing system. Beyond incorporating fundamental security measures such as hashing and secure storage of user credentials, the contemporary significance of implementing two-factor authentication is underscored. This approach significantly fortifies user data protection, thwarting most contemporary hacking methods and safeguarding against data breaches. The study acknowledges certain limitations in its approach, possibly affecting the generalizability of the findings. These limitations provide avenues for future research and exploration, contributing to the ongoing evolution of authentication methodologies in intelligent systems. Method. The two-factor authentication system integrates facial recognition technology, employing visual biometrics for heightened security compared to alternative two-factor authentication methods. Various implementations of the Siamese neural network, utilizing Contrastive loss function and Triplet loss function, were evaluated. Subsequently, a neural network employing the Triplet loss function was implemented and trained. Results. The article emphasizes the practical implications of the developed intelligent system, showcasing its effectiveness in minimizing the risk of unauthorized access to user accounts. The integration of contemporary authentication methodologies ensures a secure and robust user authentication process. Conclusions. The implementation of facial recognition technology in authentication processes has broader social implications. It contributes to a more secure digital environment by preventing unauthorized access, ultimately safeguarding user privacy and data. The study’s originality lies in its innovative approach to authentication, utilizing visual biometrics within a Siamese neural network framework. The developed intelligent system represents a valuable contribution to the field, offering an effective and contemporary solution to user authentication challenges.

Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques

Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among the tested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting and managing variations in both lighting and distance.

PROHIBITED ARTIFICIAL INTELLIGENCE PRACTICES IN THE LEGISLATION OF THE EUROPEAN UNION

The article is devoted to the study of the norms of the European Union law on Artificial Intelligence in terms of unacceptable risks of artificial intelligence. Attention is drawn to the fact that the main idea of the European approach is to support the development of trustworthy artificial intelligence, in connection with which the emphasis is placed on a risk-based approach. It is indicated that unacceptable risk is one of the levels of such an approach, which also includes artificial intelligence systems with minimal, limited and high risk. The use of unacceptable risk artificial intelligence systems refers to actions prohibited by the legislation of the European Union, except for their use in a number of circumstances specified in the law. Methods affecting the subconscious mind are considered; manipulative and misleading methods; methods using the vulnerability of a person or group of persons; methods of biometric identification; predictive methods based on profiling; methods for determining emotions. The exceptions allowed by law in relation to artificial intelligence systems of unacceptable risk are analyzed. It is concluded that the lack of broad regulatory regulation of artificial intelligence in Russia cannot be unequivocally recognized as a negative factor, since the legislator has the opportunity to analyze the results of legal approaches to regulation in foreign countries. It is concluded that in the Russian Federation there is no need to adopt a law specifically devoted to artificial intelligence. The relevant norms may well be implemented in the current industry legislation.

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.

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.

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.

Acceptance of or resistance to facial recognition payment: A systematic review

AbstractWith increasing evidence supporting the use of biometric identification methods for authentication, this study aims to enhance our understanding of the factors influencing the acceptance of and resistance to facial recognition payment (FRP) systems. To provide a comprehensive review of these factors, we conducted a systematic literature review (SLR) of empirical studies. We examined 22 key research articles from an initial pool of 1372 publications, identifying 37 factors that influence consumer acceptance or resistance to FRP. These factors were categorized into usage‐related aspects, attitudes and evaluations, user‐related traits, privacy and security concerns, and other factors. Our findings reveal that the most frequently cited factors include performance expectancy, effort expectancy, perceived usefulness, and perceived ease of use. These factors are crucial in contexts where FRP can increase productivity by providing prompt information and effective assistance. This research proposes a collective model for understanding the determinants of FRP acceptance or resistance, integrating theoretical frameworks and empirical findings. The model emphasizes the context‐dependency of user acceptance, highlighting the importance of addressing both technological and psychological factors. It incorporates usage characteristics, user characteristics, and privacy and security concerns, which are mediated by attitudes and evaluations. The proposed model provides a comprehensive framework for understanding and addressing the factors influencing acceptance of and resistance to FRP, guiding FRP service providers in developing effective strategies to increase user adoption, with future research needed to refine and assess the model further.

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.