Biometric Authentication System Research Articles

Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI

Preventing Cyber-Fraud in Nigeria's Banking System Using Fraudaeck-AI (Fraud analysis environment for cyber-fraud check) is an in-depth analysis of artificial intelligence (AI) assisted identity verification and authentication systems to prevent cyber fraud in Nigeria's banking system. The Fraudaeck (fraud analysis environment for cyber-fraud check) is a machine learning model developed to learn the interconnected subsystems of the communication network and the banking applications and how they function seamlessly to provide customers with ease and comfort of banking even on the go. An investigation revealed how this created a vulnerability in the system, allowing malicious software attacks such as the Xenomorph Trojan to gain access to the system, paving the way for cyber fraudsters—"Yahoo-boys"—to gain access. This paper proposes a solution to cyber-fraud during electronic banking transactions by using an artificial neural network model called Fraudaeck, which can be interfaced with both the telecommunication protocols and the banking application to detect network intrusion, customer identity theft, and prevent cyber-fraud to obtain hitch-free banking activities in Nigeria.

A novel technique for fingerprint template security in biometric authentication systems

A novel technique for fingerprint template security in biometric authentication systems

Identification of Biometrics Using Fingerprint Minutiae Extraction Based on Crossing Number Method

Biometrics based on fingerprint images is a self-recognition technique using fingerprint to represent a person's identity. Fingerprint is characteristic of someone's identity precisely and safely because there are no similarities and cannot be falsified. The purpose of this research is to develop a biometrics identification system based on fingerprint images by utilizing a cell phone camera for the acquisition of fingerprint images. This is based on its simplicity because almost everyone has a cell phone so that a person's identification system based on fingerprint can be used anytime and anywhere. The research was conducted using images generated from cell phone cameras with camera specifications of 2, 5 and 8 mega pixels. The method used in image processing consists of the minutiae crossing number method for the feature extraction process and the minutiae based matching method for the similarity measurement process. The results of the research concluded that cell phone cameras with specifications of 5 and 8 mega pixels can be used for the process of image acquisition in biometrics systems based on fingerprint. The feature extraction process of image results using the minutiae crossing number method and the match measurement process using the minutiae based matching method resulted in an accuracy value of 92.8% on a 5 mega pixel camera and 95.3% on an 8 mega pixel camera. The accuracy value depends on the results of the image acquisition stage, pre-processing, the threshold value in the identification process, and the number of images used in the training data in the database.

“Gesture Identifier”

Abstract: In modern society, where information security is emphasized, the importance of biometrics, which identifies individuals based on their physical and behavioral characteristics, is increasing. Hand geometry based biometric systems are gaining acceptance in low-to-medium security applications. Hand geometry -based identification systems use geometric characteristics of the hand, such as finger length and width, palm diameter and circumference. The proposed system is an authentication system that uses these hand shape features for user authentication. This project presents a low-cost, high-performance, and easy-to-use hand-geometry based biometric personal authentication system. One of the novelties of this work involves the introduction of position-based, position-independent feature extraction and hand shape identification, useful for problems related to image processing and pattern recognition. Today, the presence of students (classes) has become an important part of every organization/institution. Traditional methods of recording attendance through paper attribution or signatures are very time consuming, insecure and inefficient. This paper describes manual student attendance management in a computerized system for convenience or data reliability. Therefore, the system was developed by integrating a ubiquitous computing system into the classroom to manage student attendance with palmprint scanners

Digital Identity Verification and Management System of Blockchain-Based Verifiable Certificate with the Privacy Protection of Identity and Behavior

Due to the advantages in self-sovereignty identity management and scalability of blockchain, digital identity verification and management systems (DIVMS) of blockchain-based verifiable certificates (VC) are getting more and more attention. However, user privacy in the systems’ traditional architectures cannot be guaranteed. In this paper, the zero-knowledge succinct noninteractive arguments of knowledge (zkSNARKs) referred to as Groth16 are introduced in order to implement privacy protection of the user’s identity and behavior of DIVMS of blockchain-based VC. In the proposed architecture, the malleability attack of Groth16 is considered, and verifications of zero-knowledge proof (ZKP) and the digital signature of an identity provider (IDP) attached to VC and the status management of VC are implemented on the smart contracts of the blockchain to overcome single point failure. Furthermore, a prototype system is designed to verify the proposed architecture’s capability in privacy protection and to evaluate its performances in cost and throughput. Finally, the security of the proposed architecture is discussed, and its comparisons are conducted with those existing blockchain-based DIVMSs, especially those systems using Groth16 of zkSNARKs to improve the privacy of user. All results mentioned above have shown that the proposed system is efficient and safe, and it can improve the privacy of DIVMS of the blockchain based VC while avoiding single point failure.

Voice Based Authentication System for Web Applications using Machine Learning

Due to security concerns, the biometric trend is being used in many systems. Biometric authentication is a cheap, easy, and reliable technology for multi-factor authentication. Cryptosystems are one such example of using biometric data. However, this could be risky as biometric information is saved for authentication purposes. Therefore, voice biometric systems provide more efficient security and unique identity than commonly used biometric systems. Although, speech recognition-based authentication systems suffer from replay attacks. In this paper, we implement and analyze a text-independent voice-based biometric authentication system based on the randomly generated input text. Since the prompted text phrase is not known to the speaker in advance, it is difficult to launch replay attacks. The system uses Mel-Frequency Cepstrum Coefficients (MFCC) to extract speech features and Gaussian Mixture Models (GMM) for speaker modeling.

A Reliable Block-Chain Based Biometric Authentication Solution for Aadhar

Objectives: To propose a reliable Block-chain based Biometric Authentication Solution (BBAS) for the Aadhar biometric authentication system. Methods: We have used Sokoto Coventry Fingerprint Dataset (SOCOFing) data set for biometrics. The presented model was implemented using the Ethereum network Geth (v.1.9.25) and Solidity (v.0.6.0). Python 3.8 and Web3py were used at the client side. Findings: From the proposed solution, it is inferred that the BBAS avoids the single point of failure problem as the biometrics are distributed throughout the block-chain. Novelty: This research proposes a new hybrid scheme that uses a block-chain that stores the hash value of the biometric files and a trusted third party (Aadhar) to store the biometric files, thereby avoiding storing the same bio-metric files throughout the block-chain. Keywords: Biometric; Blockchain; Aadhar; Security; Authentication

A SURVEY PAPER ON SMART AUTHENTICATION SYSTEM FOR IDENTITY VERIFICATION

This paper presents building an effective web application for authenticating and verifying a student or employee. In today’s world, carrying various plastic smart cards to prove your identity has become an integral part of everyday life. As the world is moving towards digital technology, traditional means of verification still exist. This paper specifies the use of technology to build a smart authentication system for identity verification. There is a clear need for technological solutions for a versatile national identity for all citizens. On traditional ID cards, one can easily manipulate the information, which is not acceptable at all. For this, we are using QR codes and Face Recognition based authentication systems to identify a person. Along with that, we are providing SMS service, E-Mail service, and a Performance Chart for user info in graphical format using Machine Learning

НЕЙРОСЕТЕВАЯ МЕТОДИКА ИДЕНТИФИКАЦИИ ЛИЧНОСТИ ПО РИСУНКУ ВЕН ЛАДОНИ

The article describes the work on the creation of a neural network method for identifyinga person based on the mechanism of scanning and analyzing the pattern of palm veins as a biometricparameter. As part of the study, the prerequisites, goals and reasons for which the deve lopmentof a reliable biometric identification system is an important and relevant area of activityare described. A number of problems are formulated that are inherent in existing methods forsolving the problem: the graph method and the method based on calculating the distance expressedin various interval metrics. The description of the principles of their work is given.The tasks solved by personal identification systems are formulated: comparison of the subject ofidentification with its identifier, which uniquely identifies this subject in the information system.A mechanism for reading a pattern of veins from the palm of the hand, developed for analyzingan image obtained with a digital camera sensitive to infrared radiation, is described. When thepalm is in the frame, illuminated by the light of the near infrared range, the image obtainedfrom the camera becomes noticeable pattern of veins, vessels and capillaries that lie under theskin. Depending on the organization, the identification system may, based on the provided identifier,determine the appropriate access subject or verify that the same identifier belongs to theintended subject. Three methods for further analysis of biometric data and personal identificationare given: approaches based on categorical classification and binary classification, as wellas a combined approach, in which identification is first used by the first method, and then, bythe second, but already for a known access identifier defined on the first stage. The resultingarchitecture of the neural network for the categorical classification of the vein pattern is pr esented,a method for calculating the number of model parameters depending on the number ofregistered subjects is described. The main conclusions and experimental measurements of theaccuracy of the system when implementing various methods are presented, as well as diagrams ofchanges in the accuracy of models during training. The main advantages and disadvantages of theabove methods are revealed.

Data protection management process during remote biometric authentication

Remote biometric authentication systems have recently become widespread due to the need to use common devices and make payments over the Internet. Because biometric methods are more user-friendly and now quickly replace passwords, the task of transmitting biometric information over an open network without compromising it is becoming urgent. This work aims to upgrade the remote authentication system to increase the secrecy and security of user biometric data. In order to achieve this goal, it is proposed to use the best security methods for forming biometric templates, network steganography to increase secrecy, and the introduction of an intelligent decision-making system. These improvements will increase the security and privacy of data during the remote authentication process.

The information system for the liveness detection process using aws

Analysis of recent studies and publications. The analysis of the modern market of software and algorithmic solutions for performing the Liveness detection process showed that the currently existing approaches are completely commercial solutions with closed algorithms of their work. In addition, the Liveness detection algorithm is not yet standardized, and has many implementation options that can sometimes lead to am-biguous results [7]. That is why, it was decided to develop our own algorithm and liveness detection system based on obtaining face characteristics using the AWS API [8], because this service offers a high accuracy of face recognition, which is 99.99%, and provides 10,000 free requests to use every month, which enough to fulfill the purpose of our work. Purpose of the study. Development of the algorithm for the Liveness Detection process using AWS and the construction of a video identification system based on it. Main research material. This paper investigates the video identification of a per-son using the Liveness Detection process. The basis of the question, complexity of the Liveness Detection process and the implementation of biometric human video identifica-tion have been studied. An algorithm for conducting the Liveness Detection process based on the execution of random tasks by an identified person has been developed. Integration with the Amazon DetectFaces API was carried out in order to obtain the character of a digital image of the head, as a result of which it becomes possible to analyze a photo of a person for a wide range of key facial features. A manual test of the Amazon DetectFaces API was conducted, in the process of which, empirically, thresh-olds of facial characteristics in the image for the Liveness Detection process were set. Integration with the Amazon CompareFaces API was implemented to execute the bio-metric video identification. The testing of the developed Liveness Detection application system based on the created Liveness passing algorithm and selected AWS API thresholds consisted of 100 tests of different orientations, from direct user-flow testing to tests with a medical mask, glasses and beard, a photo of a face and a video image of a person for the purpose of forgery his personality. Thanks to the initial accuracy of Amazon API facial recognition of 99.99% and the random selection of tasks in the developed Liveness Detection algo-rithm, all tests gave the expected result - the person was either successfully video-identified at the enterprise, or his identity was faked during the Liveness Detection pro-cess. Conclusions. Based on the research, the software system of biometric human video identification has been developed, which allows to automatically establish the presence of a person in front of a video camera and identify it, preventing the use of fake artifacts to falsify a person's identity.

A hybrid BTP approach with filtered BCH codes for improved performance and security

Authentication of users through their biometric identity has continuously shown ever-increasing importance in our daily lives. However, since biometric data is permanently associated with the users, any loss of this data would be disastrous because it is irrevocable and irreplaceable, unlike traditional passwords and tokens. Therefore, cancelable transformation and bio-cryptosystem are the two main methods used to solve the security and privacy issues associated with biometric authentication systems while preserving accuracy. In this paper, we combined both techniques for biometric template protection (BTP), using the merits and advantages of each approach to compensate for the weaknesses of the other. Specifically, fixed-length vector features generated through kernelized learning are canceled using index-of-max hashing. As a result, a Cancelable Transformation is achieved. The acquired canceled vectorial pairs are then fed into a Bio-Cryptosystem based on fuzzy symmetric encryption and filtered BCH (fBCH) codes for encryption-decryption key pairs and key encoding-decoding, respectively. Hence, double-layered protection coupled with efficient error correction is achieved via symmetric encryption and hashing of the projected vectorial features. The proposed method ensures the creation of parity bits from the adjusted fBCH codewords to fit the length of the canceled vectors and correct the errors associated with the codewords. This allows us to retrieve the precise secret information with a high probability for a genuine user while returning a null or relatively less probability for an imposter user. Extensive experiments on eight FVC2002, FVC2004, and FVC2006 fingerprint datasets are carried out to validate the scheme's performance. Finally, the revocability, unlinkability, and security analysis are experimentally validated.

Privacy preserving steganography based biometric authentication system for cloud computing environment

Recently, cloud computing (CC) received significant interest among organizations and individuals. Despite the significant benefits of CC, security and privacy are considered as the major issue. Biometric authentication is commonly employed for authentication purposes and has gained attention among several researchers due to its stable and high recognition rate. Amongst the several biometric authentication models, fingerprint is treated as an effective one to achieve security and privacy. Besides, image steganographic approaches are applied in order to enhance the security of the biometric data. The state of art biometric data hiding techniques generally performs the data embedding on the region which does not encompass key features of the biometrics. With this motivation, this paper presents a privacy preserving steganography based biometric authentication system (PPS-BAS) for cloud environments. The goal of the PPS-BAS model is to hide the fingerprint image (secret image) into the eye retina image (cover image) and transmits it to the cloud in an encrypted way. The proposed PPS-BAS model involves multilevel discrete wavelet transform (DWT) technique to split the cover image in order to identify the pixel location. Besides, continuous pigeon inspired optimizer (CPIO) algorithm is applied to determine the optimal pixel points in the cover image. At the same time, a Q-learning technique is employed to extract the minutiae from the fingerprint image and is then hidden into the optimal pixel locations in the cover image. In order to further increase security, double-logistic chaotic map (DLCM) model is applied for encrypting the stego image which is then transmitted to the cloud server. After the reconstruction of the original fingerprint image (secret image), the biometric recognition process takes place using the Scaled Conjugate Gradient (SCG) based back propagation neural network (BPNN) model. A detailed simulation analysis is performed to highlight the enhanced outcomes of the proposed PPS-BAS model and the comparative results analysis ensured the betterment of the PPS-BAS model over the recent state of art biometric authentication systems.

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks.

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.

BAED: A secured biometric authentication system using ECG signal based on deep learning techniques

Biometric authentication technology has become increasingly common in our daily lives as information protection and control regulation requirements have grown worldwide. A biometric system must be simple, flexible, efficient, and secure from unauthorized access. The most suitable and flexible biometric traits are the face, fingerprint, palm print, voice, electrocardiogram (ECG), and iris. ECGs are difficult to falsify among these biometric traits and are less attack-prone. However, designing biometric systems based on ECG is very challenging. The major limitations of the existing techniques are that they require a large amount of training data and that they are trained and tested on an on-person database. To cope with these issues, this work proposes a novel biometric authentication scheme based on ECG detection called BAED. The system was developed based on deep learning algorithms, including a convolutional neural network (CNN) and a long-term memory (LSTM) network with a customized activation function. The authors evaluated the proposed model with on-and off-person databases including ECG-ID, Physikalisch-Technische Bundesanstalt (PTB), Check Your Bio-signals Here Initiative (CYBHi), and the University of Toronto Database (UofTDB). In addition to the standard performance parameters, certain key supportive identification parameters such as FMR, FNMR, FAR, and FRR were computed and compared to increase the model’s credibility.The proposed BAED system outperforms prior state-of-the-art approaches.

Face and gait biometrics authentication system based on simplified deep neural networks

Face and gait biometrics authentication system based on simplified deep neural networks

Facial identity protection using deep learning technologies: an application in affective computing

Automatic prediction of human attributions of valence and arousal using facial recognition technologies can improve human–computer and human–robot interaction. However, data protection has become an issue of great concern in affect recognition using facial images, as the facial identities of people (i.e. recognising who a person is) could be exposed in the process. For instance, malicious individuals could exploit facial images of users to assume their identities and infiltrate biometric authentication systems. Possible solutions to protect the facial identity of users are to: (1) extract anonymised facial features in users’ local machines, namely action units (AU) of facial images, discard their facial images and send the AUs to the developer for processing, and (2) employ a federated learning approach i.e. process users’ facial images in their local machines and only send their locally trained models back to the developer’s machine for augmenting the final model. In this paper, we implement and compare the performance of these privacy-preserving strategies for affect recognition. Results on the popular RECOLA affective datasets show promising affect recognition performance in adopting a federated learning approach to protect users’ identities, with Concordance Correlation Coefficient of 0.426 for valence and 0.390 for arousal.

Gendered identities and digital inequalities: an exploration of the lived realities of the transgender community in the Indian digital welfare state

ABSTRACT With rapid digitalisation and technological advancements, the emergence of digital welfare states worldwide has become a reality. Broadly, the term digital welfare state refers to the adoption of information and communication technologies (ICTs) and digital tools to transform public and welfare services, such as biometric identification systems and automated systems to verify the eligibility of citizens for welfare benefits. This increased use of technological innovations and digital tools in governance is presented as a citizen-centric move that would improve accessibility and availability, and increase efficiency. However, one of the major critiques of the digitalisation of welfare systems is that it excludes people from disadvantaged sections who lack access to digital infrastructure and digital literacy from actively participating in society. In the context of the emerging Indian digital welfare state, this paper focuses on a particular marginalised community, that is, the transgender community in India, to understand their lived experiences of interacting with public digital systems. Historically, the transgender community in India has been socioeconomically marginalised, making them important beneficiaries of public welfare services. The Indian government recently introduced a gender identification digital system for the transgender community, which would be used to procure official gender identity cards and dispense social benefits and subsidies to them. This paper analyses the inequalities and exclusions faced by the transgender community in India in participating in the digital welfare system and highlights its potential human rights and policy implications.

ИДЕНТИФИКАЦИЯ ЧЕЛОВЕКА ПО БИОМЕТРИЧЕСКИМ ДАННЫМ: ОБЗОР СОВРЕМЕННЫХ ТЕХНОЛОГИЙ

Biometric identification systems have now become quite widespread, since they use not specialized physical media for recognition, but signs or features of the person himself, which makes it possible to reliably identify a person for a given purpose. Biometric identification is the process of comparing and determining the similarity between a person's data and his biometric "template". Biometrics allows you to identify and verify a person based on a set of specific and unique traits inherent in him from birth. This article discusses certain types of biometric data to be identified, problems and prospects of their application.