Biometric Authentication System Research Articles

A new approach to develop biometric fingerprint using human right thumb fingernail

In this article, we analyzed the structure of human nails to develop a modern biometric authentication system based on the nail bed and finger lunula. The results of the studies on the collected images proved that each fingernail has distinctive characteristics in terms of the length and width of the nail and the lunula, even identical twins. We focused on the fingernail of the right thumb because of its large size and the accuracy and clarity of the nail. The mediation of the indicative points on the nail bed and on the lunula was used to form the pentagonal structure and use it as a region of interest. Then an ensemble independent component analysis, principal component analysis, haar wavelet, and scale invariant feature transformation, were used. Later we classified these algorithms using support vector machine and Naive Bayes techniques, the performance of each algorithm was analyzed by feature extraction with two classifiers. This study was conducted on 100 participants and showed that this new method could be used as a biometric identification system for humans. There was no similarity in results for all verified samples.

Biometric Authentication System

Abstract: Biometric authentication systems have gained significant interest due to their reliable and practical authentication options. These systems utilize physiological or behavioral traits, like fingerprints, face recognition, and speech patterns, to confirm a person's identity. They offer advantages such as increased security and user convenience compared to traditional methods. However, biometric authentication also poses challenges, including privacy concerns and the potential for false positives or negatives. This research study provides a comprehensive overview of the various forms, applications, benefits, and limitations of biometric authentication systems. It emphasizes the need to address privacy concerns and establish appropriate standards and regulations. The findings inform practitioners, researchers, and policymakers about the latest advancements and implications of biometric authentication systems.

Advancements in Biometric Authentication Systems: A Comprehensive Survey on Internal Traits, Multimodal Systems, and Vein Pattern Biometrics

Advancements in Biometric Authentication Systems: A Comprehensive Survey on Internal Traits, Multimodal Systems, and Vein Pattern Biometrics

Privacy Threats in Facial Recognition-Based Identity Verification

Facial recognition technology has gained significant prominence in identity verification systems, providing convenience and security in various domains. However, the widespread adoption of this technology also raises significant concerns regarding privacy threats. This paper presents a comprehensive privacy threat model specifically designed for identity verification based on facial recognition. The model encompasses potential adversary objectives, such as unauthorized access, identity theft, and profiling, and identifies specific privacy threats, including unauthorized data access, biometric data misuse, tracking and surveillance, re-identification attacks, discrimination and bias, and data retention and sharing. To mitigate these threats, the paper proposes several mitigation measures, such as informed consent, transparent data handling, secure data storage and transmission, minimization of data collection, regular audits and assessments, and ethical considerations. By providing a holistic view of privacy threats in facial recognition-based identity verification, this model aims to guide researchers, practitioners, and policymakers in developing and implementing effective privacy protection mechanisms in this rapidly evolving technological landscape

Efficient thermal face recognition method using optimized curvelet features for biometric authentication.

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user's face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

The reversibility of cancelable biometric templates based on iterative perturbation stochastic approximation strategy

Researchers have been proposing different matching frameworks for cancelable template design to improve the matching performance and security of biometric authentication systems. Despite the advantages provided by these systems, they are often vulnerable to different attacks due to their templates' invertible nature. This study utilized an iterative perturbation stochastic approximation procedure to analyze the irreversible order-based encoding approach used in cancelable biometric template transformation. The strategy begins by exploiting the scores corresponding to the encoded words, then a mixed variable-based iterative perturbation is used to generate independent random elements and consequently the corresponding template from the scores. The strategy exploits the vulnerability of three different cancelable systems it has tested, demonstrating that other techniques of similar nature fall short of the security standard for attack mitigation that cancelable biometric systems require. After analyzing the reasons that exploit the system's vulnerability, we use a parameterized thresholding non-uniform quantization as a countermeasure to boost the system's robustness while maintaining a balanced performance-security trade-off. As a result, the system can evade attacks without significantly hindering its matching performance. Finally, generality and computational complexity evaluations on different modalities and strategies validate the attack's efficacy and realism, respectively.

Restrictive Voting Technique for Faces Spoofing Attack

Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications.

The effect of individual stress on the signature verification system using muscle synergy

Biometric authentication systems, in terms of using the biometric characteristic, and because these indicators are resistant to the passage of time, and environmental, and physical changes of people, in most different situations from the environment or changes in emotional factors of person, perform identity verification with optimal accuracy. In the signature verification systems, people may experience stress caused by environmental or internal factors when registering a signature, this stress can affect the performance of the signature verification system in terms of accuracy and stability. We try to study the performance of a signature verification system based on a biometric characteristic (muscle synergy patterns) for people who are stressed while signing.For this purpose, electromyography (EMG) signals were recorded from hand and arm muscles of people while signing. Then, using Non-Negative Matrix Factorization (NMF) method to extracted muscle synergies from EMG signals after pre-processing. The extracted synergy patterns are classified into genuine and forgery classes by Support Vector Machine (SVM) classifier. Furthermore, the confirmation of the authentication of stressed people was studied using this method. Finally, the results obtained were compared to the results obtained from the signature verification system for unstressed people and using K-means classifier method.

Using a lightweight Siamese neural network for generating a feature vector in a vascular authentication system

The article analyzes the possibility of using a Siamese convolutional neural network to solve the problem of vascular authentication on an embedded hardware platform with limited computing resources (Orange Pi One). The authors give a brief review of modern methods for calculating image feature vectors used in the tasks of classifying, comparing or searching for images by content: based on variational series (histograms), local descriptors, singular point descriptors, descriptors based on hash functions, neural network descriptors. They suggest using the architecture of a biometric authentication system (BAS) based on images of palms in the visible and near-IR spectra based on a Siamese convolutional neural network. The developed software solution allows using the Siamese neural network in the "full network" (both symmetrical channels of the neural network are used) and "half of the neural network" (only one channel is used) modes to reduce the time for comparing biometric data vectors - images of the palms of registered BAS users. The authors demonstrate advantages of the neural network features: universality, scalability and competitiveness, including on embedded hardware and software solutions with limited computing resources without graphics accelerators. The studies have shown that using the Siamese neural network, the "overall accuracy" of palm image classification can be improved from 0.929 to 0.968 when compared with the image vectorization method based on a perceptual hash, while showing a comparable authentication time for individuals registered in BAS. In the experiments, the authors use a database of 2,000 images for 400 people.

English

A multi modality biometric authentication system can combine information from various modalities and provides accurate results compared to biometric systems used individually. A novel ensemble classifier-based multimodal biometric authentication system has been proposed in this work. The performance of the proposed multimodal authentication system is measured using parameters such as Accuracy, Sensitivity and Specificity and compared with the SVM classifier, Decision tree classifier when fingerprint, Iris, and Face features are used. The results of the multimodal biometric system are also compared with the biometric authentication system when fingerprint features are used and combined with Fingerprint & Iris features. The proposed ensemble classifier-based multimodal biometric authentication system provides an accuracy of 96.75%, Sensitivity of 94.74%, Specificity of 98.95%, FAR of 1.04 and FRR of 5.26. The proposed ensemble classifier outperforms SVM and decision tree classifiers regarding performance measures.

A novel privacy-preserving biometric authentication scheme.

Most existing secure biometric authentication schemes are server-centric, and users must fully trust the server to store, process, and manage their biometric data. As a result, users' biometric data could be leaked by outside attackers or the service provider itself. This paper first constructs the EDZKP protocol based on the inner product, which proves whether the secret value is the Euclidean distance of the secret vectors. Then, combined with the Cuproof protocol, we propose a novel user-centric biometric authentication scheme called BAZKP. In this scheme, all the biometric data remain encrypted during authentication phase, so the server will never see them directly. Meanwhile, the server can determine whether the Euclidean distance of two secret vectors is within a pre-defined threshold by calculation. Security analysis shows BAZKP satisfies completeness, soundness, and zero-knowledge. Based on BAZKP, we propose a privacy-preserving biometric authentication system, and its evaluation demonstrates that it provides reliable and secure authentication.

Retraction Note: Multi-modal biometric authentication system based on face and signature using legion feature estimation technique

Retraction Note: Multi-modal biometric authentication system based on face and signature using legion feature estimation technique

Generation of Secure Fingerprint Template Using DFT for Consumer Electronics Devices

Biometric authentication systems are being extensively used to secure various applications. Consumer electronics devices also utilize biometrics as one of the authentication methods. However, this growth in the use of biometric based authentication may lead to the permanent loss of the biometric identity of a person if it is compromised, as biometric data cannot be changed. Hence, this poses the requirement of securing user templates in biometric based systems. In this paper, we propose a secure, alignment-free, and non-invertible fingerprint based user template protection technique which utilizes the mapping of pair-polar structures of minutiae in a 3D-grid and Discrete Fourier Transform (DFT). The proposed technique has been analyzed in terms of revocability, diversity, security, and performance by using four publicly available fingerprint databases of Fingerprint Verification Competition (FVC) 2002 and 2004. In addition to these databases, a partial fingerprint database generated from the FVC2002 DB1 is also utilized. The Equal Error Rate (EER) values of the proposed technique for all the databases are compared with that of the state-of-the-art techniques, and the superiority of the results depicts the robustness and effectiveness of the proposed technique.

Privacy-Preserving Preselection for Protected Biometric Identification Using Public-Key Encryption With Keyword Search

The efficiency of biometric systems, in particular efficient and accurate biometric identification, is one of the most challenging open problems in biometrics today. Adding to that, biometric data are sensitive data deserving adequate protection. As a solution, this work proposes an efficient privacy-preserving reduction of the computational workload of biometric identification systems using public-key encryption with keyword search (PEKS). For long-term protection of the biometric data, fully homomorphic encryption is applied for template protection. As all applied cryptographic schemes are lattice-based, they also offer post-quantum security. Throughout the system, the recognition accuracy of the unprotected system is preserved. In an evaluation on a public face database, the computational workload of an identification search in the encrypted domain is reduced down to 8.4% compared to an exhaustive search, achieving identification on 1062 subjects in 210 milliseconds. Based on these results, an identification search on 1 million subjects can be estimated at under 3 minutes using off-the-shelf hardware.

A Research Paper on Smart Authentication System for Identity Verification

Abstract: This research paper focuses on the creation of aweb application that employs modern techniques to authenticate and verify the identities of students and employees. In the era of digital transformation, conventional identification methods have become less effective. To overcome this challenge, the proposed solution leverages advanced biometric technologies like facial recognition and QR codes to ensure precise identification of individuals. Moreover, the application offers SMS and email notifications, along with a performance chart generated by machine learning to provide comprehensive user information.The

Variations in Shape of Tongue among Students attending Chitwan Medical College

Introduction: The need for a new biometric system is gaining importance. Tongue print is also getting acceptance as a system for biometric authentication. The tongue is an extremely muscular organ used for communication, taste, and deglutition. Studies have shown variations in the morphology of the tongue. Studies pertaining to the morphology of the tongue is lagging in Nepal. Hence this study was carried out to assess the various morphological variations of the tongue such as shapes, borders, and color among the bachelor level students in a tertiary care center in Nepal Methods: A descriptive cross-sectional study was conducted among 312 bachelor-level students of Chitwan Medical College. The convenience sampling method was used to collect the data. All the data was recorded in a proforma. The sociodemographic details of the participants were recorded. The borders, colors, fissures, and shapes of the tongue’s morphology were documented. The collected data were entered into Microsoft excel 2019 and transported into Statistical Package for Social Science (SPSS) version 16. The data was then analyzed using descriptive statistical methods. The data was also calculated in form of frequency and percentage and later on, they were presented in form of a table. Results: Among 312 students who took part in the study, 126 (40.4 %) of the participants were male and 186 (59.6 %) of them were female. The mean age of participants was 22.38 ±2.008 years. U-shaped tongue was prevalent followed by V-Shape tongue and bifid tongue. More than half of the participants 180 (57.7 %) had partially scalloped tongues. While comparing gender wise U-shaped tongue was more common in both males and females. The bifid tongue was observed more in males 11 (8.7 %) while the V-shaped tongue was more in females 52 (28 %). Conclusions:

A Study of Approaches and Measures aimed at Securing Biometric Fingerprint Templates in Verification and Identification Systems

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

Gait recognition methods in the task of biometric human identification

This article focuses on defining the problem of solving the problem of human identification by means of gait recognition in bi-ometric identification systems.In order to determine the prospects of using gait recognition methods for human identification, a gen-eralized model of a biometric identification system was considered, the main modules of the system were identified and a brief de-scription of each module was provided.Next, the basic requirements for human biometric features were identified, the main bio-metric features were considered, and the features of their use in biometric identification systems were determined.The issue of using gait as a biometric identifier was also considered. It has been determined that the use of human gait allows to get rid of two main obstacles in the construction of biometric identification systems: users are not required to provide personal biometric information in advance, and the system does not require specialized equipment.Also, the issue of multi-view gait recognition was considered. Mul-ti-view gait recognition involves capturing gait data from different angles and using this data to improve recognition accuracy.This approach has shown great promise in challenging scenarios such as low lighting conditions.Next, we analyzed scientific works in the field of gait recognition. It was determined that gait recognition methods can be divided into template-based and non-template-based methods.Template-based methods are aimed at obtaining patterns of torso or leg movements, i.e. they usually focus on the dynamics of movement in space or on spatio-temporal methods. Non-template-based methods consider shape and its features as more relevant characteristics, i.e., human recognition areperformed using measurements that reflect the shape of the person.Next, we consider the use of different datasets in the process of training and testing human gait recognition methods. The main datasets were identified and their characteristics and features were collected.We considered the presence of various characteristics in the datasets, as well as the means of representing information about human gait.The researchhas identified the main problems and challenges facing research-ers in this area, as well as the main trends in the field of human gait recognition in biometric identification systems.

Utilizing Convolutional Neural Networks for Fingerprint-Based Attendance Monitoring

The traditional method of taking attendance using paper sheets is prone to errors like impersonation, loss, or theft. To solve this issue, automatic attendance systems utilizing identification technology such as barcode badges, electronic tags, touch screens, magnetic stripe cards, and biometrics have been implemented. Biometric technology uses physiological or behavioral characteristics for identification purposes, but traditional biometric systems have limitations such as vulnerability to damage or alteration over time, and variations in occlusions, poses, facial expressions, and illumination can affect face recognition accuracy. Fingerprint identification relies on the distinctiveness of fingerprints and involves comparing two impressions of the friction ridges on human fingers or toes to determine if they belong to the same individual. There are five primary categories of fingerprints: arch, tented arch, left loop, right loop, and whorl. Various algorithms have been developed to recognize fingerprints using minutiaebased matching, which involves identifying key features like ridge ending and bifurcation. Deep learning algorithms, particularly convolutional neural networks, have been successful in improving identification accuracy by extracting features automatically from fingerprint images. In recent times, securing personal data has become increasingly important, and the Convolutional Neural Network (CNN) identification system is recommended for improving accuracy and performance. This paper proposes a fingerprint identification system that combines three models: CNN, Softmax, and Random Forest (RF) classifiers. The conventional system uses K-means and DBSCAN algorithms to separate the foreground and background regions and extracts features using CNNs and dropout approach. The Softmax acts as a recognizer. The proposed algorithm is evaluated on a public database and shows promising results, providing an accurate and efficient biometric identification system.

Evaluation of Spatial Spillover Effect of Multidimensional Hybrid Financial Risk Contagion Based on the DAI Spatial Econometric Model

The process of global integration has accelerated, and the international financial market has become increasingly closely linked. The financial risks that come with them are becoming more complex and difficult to guard against. Multimedia modeling in Health Cloud biometric authentication and data management systems can be applied to the analysis of financial markets. Most of the current financial risk analysis models are based on a single time, and the models are relatively simple and cannot adapt to the current complex multidimensional mixed financial risk environment. Therefore, this paper aimed to analyze the spatial spillover effect of financial risk contagion based on the directed asymmetric (DAI) spatial econometric model. This paper proposed to transfer entropy information weight information and introduce the GARCH (generalized auto-regressive conditional heteroskedasticity) model to improve the traditional econometric model. By constructing a DAI measurement model, the spatial contagion of multidimensional mixed financial risks was analyzed, and on this basis, a generalized multidimensional economic space was established to analyze spatial spillover effects and analyze the specific path of spatial spillover effects. The model results in this paper showed that the degree of correlation between the stock and bond market varied greatly between countries. Among them, the change coefficient W s ⟶ b r s D of the event period was judged to have a large degree of negative change in the United Kingdom, Germany, and France in the European Union, which were −0.9885, −0.9876, and −0.9748, respectively. This showed that the model in this paper had a good and reliable ability to cope with the current multidimensional mixed complex financial risk environment and could be used as a reference for financial risk-related research. At the same time, it also proved that multimedia modeling in health cloud biometric authentication and data management system could provide a role in financial risk contagion analysis.