Biometric Authentication Techniques Research Articles

Performance evaluation of efficient segmentation and classification based iris recognition using sheaf attention network

Iris recognition, a precise biometric identification technique, relies on the distinct epigenetic patterns within the iris. Existing methods often face challenges related to segmentation accuracy and classification efficiency. To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). Main objective is to develop an integrated framework that optimizes iris segmentation and classification. Subsequently, dense extreme inception multipath guided up sampling network is employed for accurate segmentation. Finally, classifiers including convolutional neural network with sheaf attention networks are evaluated. The findings indicate that the proposed method achieves superior iris recognition accuracy and robustness, making it suitable for applications such as secure authentication and access control. By comparing with existing approaches CSAN obtains 99.98%, 99.35%, 99.45% and 99.65% accuracy for the four different proposed datasets respectively.

Leveraging Face Recognition Technology for Secure ATM Transaction

ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transactions with ATMs is increasing day by day. ATMs are a very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including fingerprinting, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holders to verify the identity of unauthorized users through some dedicated artificial intelligent agents, for remote certification. However, it is obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone to have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy

An Efficient, Fast and Accurate Online Signature Verification Using Blended Feature Vector and Deep Learning

Online signature verification is one of the biometric authentication techniques. Online signature verification reduces the human error that may occur due to physical signature verification. Many researchers have provided a method of online signature verification still there is a scope for improvement. In this paper, a novel blended feature vector (BFV) is formed by combining two feature vectors. The first of these feature vectors is formed using a raw online signature database and the other feature vector is formed of the features extracted from the grayscale images obtained from the signature database. The algorithm makes use of the probability distribution of the features to provide a feature vector that results in an enhanced verification system. Two online signature databases, namely SVC2004 and ATVS-SSig are used. BFV is used to train the proposed mixed sequence deep neural network (MS-DNN). The use of a blended feature vector in the training of MS-DNN, instead of a whole online signature database, improves the training time of deep neural network to a great extent. This reduction in training time might be useful for new user registration. The performance parameters considered are validation efficiency and equal error rate. Results are compared with the existing state of technology and the comparison shows that the use of a blended feature vector as a classification vector improves the validation efficiency (99.5%) and also there is an improvement in the verification success rate in terms of equal error rate (EER 1.5%) as compared to the existing research.

Transforming online voting: a novel system utilizing blockchain and biometric verification for enhanced security, privacy, and transparency

As a cornerstone of democratic governance, elections hold unparalleled significance, shaping a nation’s trajectory. However, the prevailing ballot-paper based voting systems continue to face trust issues among significant populations. As a result, e-Voting has emerged as an appealing alternative, with numerous countries opting for its implementation globally. While e-Voting systems offer several advantages, they also come with their own set of challenges. Even a minor vulnerability can lead to massive manipulations in voting results. In recent years, there have been efforts to revolutionize the e-Voting paradigm by harnessing the potential of emerging technologies such as biometrics and blockchain. This paper proposes a Internet-based voting that adopts blockchain technology and biometric identification techniques. We use biometric modalities, such as fingerprint and facial recognition, for voter authentication while leveraging Hyperledger Fabric framework as blockchain network and ensuring a secure, transparent, and tamper-evident voting record. We demonstrate the proposed system with 100 participants in a preset environment where we collect the biometrics data. The results indicate that 87% of participants successfully registered with biometrics, while 88% cast their votes with a combination of either voter ID and fingerprint or voter ID with facial recognition. Our findings suggest that the proposed system allows voters to access the system seamlessly and automate identity verification procedures while ensuring a secure, decentralized, and distributed database network that maintains transparency. Future research shall be carried out in collaboration with election officials and voters to improve the system in real-world scenarios.

Analysis of Biometric Authentication Techniques: A Review

This report delves into diverse biometric authentication techniques and offers an overview of their evolution, applications, limitations, advancements, and comparative attributes. It also highlights biometric authentication's pivotal role in elevating security and convenience across sectors.

Towards Secure Identification: A Comparative Analysis of Biometric Authentication Techniques

This paper provides an overview and analysis of existing biometric authentication methods. Biometric authentication enhances the security of traditional authentication methods by utilizing unique biological characteristics of individuals, such as fingerprints, facial features, or iris patterns, to verify identity. However, implementing biometric authentication may involve initial setup costs for service providers due to the need for specialized hardware and software. Additionally, users may experience some initial inconvenience during the enrollment process to register their biometric data. This paper presents a comparative analysis of the usability of biometric authentication techniques. Initially, a survey of existing work is conducted by comparing techniques, algorithms, and metrics used in research. Later the challenges of the distinct types of authentication techniques are discussed, and their problems are discovered. Subsequently, the findings of a quantitative study based on a are presented, aimed at assessing the usability of those techniques. The findings indicate that authentic biometric techniques are perceived as usable.

Data Collection Module for a Centralized Electronic Health Records System

Electronic Health Records (EHRs) play a crucial role in modern healthcare, enhancing quality, safety, efficiency, and research capabilities. However, in Nigeria the full acceptance of such modern ways of managing healthcare delivery is marred with various challenges such as infrastructure, funding, technical expertise, and regulatory issues. This paper presents a proposed conceptual framework of a Data Collection Module for a Centralized EHR System in Nigeria, aimed at addressing data accessibility challenges faced by healthcare facilities for patients registering at their facilities. The module utilizes National Identification Numbers (NINs), barcodes, and biometric identification methods to collect and secure patient demographic data. It incorporates a web application, biometric software, and barcode/QR code software to ensure efficient data collection and verification. Our study demonstrates the feasibility and effectiveness of employing biometric identification techniques in data collection modules, offering enhanced accuracy, reliability, security, and usability of patient data in a developing healthcare system like Nigeria.The proposed module has not been tested in a real-world environment, so more study is required to determine its viability, scalability, and effect on the standard and results of healthcare.

An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization

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

New Face Recognition System Based on DCT Pyramid and Backpropagation Neural Network

Face recognition has emerged as a prominent biometric identification technique with applications ranging from security to human-computer interaction. This paper proposes a new face recognition system by appropriately combining techniques for improved accuracy. Specifically, it incorporates a discrete cosine transform (DCT) pyramid for feature extraction, statistical measures for dimensionality reduction of the features, and a two-layer backpropagation neural network for classification. The DCT pyramid is used to effectively capture both low- and high-frequency information from face images to improve the ability of the system to recognise faces accurately. Meanwhile, the introduction of statistical measures for dimensionality reduction helps in decreasing the computational complexity and provides better discrimination, leading to more efficient processing. Moreover, the two-layer neural network introduced, which plays a vital role in efficiently handling complex patterns, further enhances the recognition capabilities of the system. As a result of these advancements, the system achieves an outstanding 99 % recognition rate on the Olivetti Research Laboratory (ORL) data set, 98.88 % on YALE, and 99.16 % on AR. This performance demonstrates the robustness and potential of the proposed system for real-world applications in face recognition.

Leaking my face via payment: Unveiling the influence of technology anxiety, vulnerabilities, and privacy concerns on user resistance to facial recognition payment

As biometric identification techniques for authentication continue to gain prominence, the demand for mobile payment systems employing facial recognition payment (FRP) in diverse business contexts has increased significantly. Leveraging the antecedent-privacy concern-outcome (APCO) model, this study constructs a comprehensive research framework for FRP and subjects it to examination through an online survey involving 1058 Chinese users. The study's results reveal that technology anxiety exhibits significant relationships with technology, provider, legal, and user vulnerabilities. However, it is noteworthy that technology anxiety does not exert an influence on privacy concerns. Conversely, technology, provider, legal and user vulnerabilities are found to be significantly associated with privacy concerns. Furthermore, the research highlights that privacy concerns have a positive association with user resistance to FRP. The current study also provides practical implications for cooperation and governments.

A Fast Iris Liveness Detection for Embedded Systems using Textural Feature Level Fusion Algorithm

Iris recognition is a widely used biometric authentication technique due to its high accuracy and uniqueness. However, iris recognition systems are susceptible to attacks using fake or synthetic iris images, causing a serious security threat. To address this issue, this paper presents a fast iris liveness detection method specifically designed for embedded systems. The proposed method utilizes a textural feature level fusion algorithm using Local Binary Pattern (LBP) and Gray-Level Co-Occurrence Matrix (GLCM) to distinguish between live and printed iris images. LBP captures texture information, while GLCM characterizes the statistical properties of the iris images. By combining these complementary features, the proposed method enhances the discrimination capability and robustness against presentation attacks. Furthermore, to enable real-time and efficient implementation, the proposed liveness detection is optimized and implemented for embedded systems. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in accurately detecting iris liveness. The proposed fast iris liveness detection is implemented and optimized on C++ which can be complied and deployed in various embedded devices for iris recognition systems on real-world applications, such as access control, biometric authentication, and surveillance systems.

Information Extraction for Touch Free Biometric Authentication System from Large Databases using Deep Learning Algorithm

Users' privacy must be strictly protected because smartphones have become an essential tool for storing sensitive data. This may be performed by using the most accurate and trustworthy bio-metric authentication techniques available, like iris, voice, facial and fingerprints cognizance. This paper presents a way to develop and test biometric recognition system for smartphones. Touch-less biometric recognition system is an implementation of biometric verification and identification system on mobile phones that utilizes mobile phone cameras and microphone to acquire the biometrics of the user. Once the data is captured, they are pre-processed using standard pre-processing algorithm to obtain the skeleton. This is used to extract true minutiae eliminating all the false minutiae. The usage of mobile phones for biometric recognition makes it quite convenient and feasible for everyone. Also, touch-less system has a lot of advantages over touch-based ones and hence, they become a more popular choice.

Efficient Iris Image Recognition System Based on Machine Learning Approach

Iris identification is a well‐known technology used to detect striking biometric identification techniques for recognizing human beings based on physical behavior. The texture of the iris is essential and its anatomy varies from individual to individual. Humans have distinctive physical characteristics that never change. This has resulted in a considerable advancement in the field of iris identification, which inherits the random variation of the data and is often a dependable technological area. This research proposes three algorithms to examine the classifications in machine learning approaches using feature extraction for the iris image. The applied recognition system used many methods to enhance the input images for iris recognition using the Multimedia University (MMU) database. Linear Discriminant Analysis (LDA) feature extraction method is applied as an input of three algorithms of machine learning approaches that are OneR, J48, and JRip classifiers. The result indicates that the OneR classifier with LDA achieves the highest performance with 94.387 % accuracy, while J48 and JRip reached to 90.151% and 86.885% respectively. Index Terms— Iris recognition, LDA, OneR, J48, JRip.

Identification of Finger Vein Images with Deep Neural Networks

To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.

Finger Vein Recognition Technology: Principles, Applications, and Future Prospects

Finger vein recognition technology is a biometric identification technique that analyzes the vein patterns in the fingers to authenticate individuals. This paper aims to provide a comprehensive overview of the principles, applications, and future prospects of finger vein recognition technology. Firstly, the unique biological characteristics and advantages of finger vein recognition will be explored. Secondly, the working principle and key techniques of the finger vein recognition system will be introduced, covering image acquisition, feature extraction, and matching algorithms. Subsequently, the extensive applications of finger vein recognition technology in personal identity authentication, financial payments, information security, and other domains will be discussed. Finally, the future development of finger vein recognition technology will be forecasted, addressing potential challenges and solutions, as well as its potential applications in broader domains.

ONLINE BANKING SECURITY WITH REAL TIME FACE RECOGNITION APPROACH

Online banking has become an essential part of modern banking. Many attacks are successful in accessing social network accounts since the current password-based authentication paradigms are not efficient and robust enough as well as vulnerable to automated attacks. The simplest alternative is complementing the single factor (password-based) authentication process with additional identification elements, such as one-time PIN codes, generated by the user’s own device (e.g., the smartphone) or received via SMS. To improve the security of online banking transactions, real-time face recognition technology can be used as a biometric authentication technique. This technology provides a reliable and convenient way of verifying the identity of customers in real-time. The aim of this project is to develop an online banking system that uses real-time face recognition technology for customer authentication. The system will be designed to provide a secure and user-friendly interface that allows customers to carry out banking transactions such as funds transfer, bill payments, and balance inquiries. The system will incorporate a Grassmann Learning algorithm that is capable of capturing and analyzing a customer's facial features in real-time. The algorithm will compare the facial features of the customer with those in the bank's database to verify the customer's identity. Second level verification based on OTP verification in reverse order. The system will provide a secure and user-friendly interface for customers to carry out banking transactions in real-time. In this proposed net banking application notifications are sending to the user regarding banking interface access and amount transaction.

Multimodal Bank Transaction System With Real-Time Secure Clickbait and Biometric ATM User Authentication

ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transaction with ATM is increasing day by day. ATM is very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including finger printing, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holder to verify the identity of unauthorized user through some dedicated artificial intelligent agents, for remote certification. However, it obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy. By using this real time dataset, the proposed system achieves the highest accuracy with 97.93%

Expert Consensus on ECG Identification Applied in the Insurance Industry✰

Electrocardiograms (ECGs) have the potential to be used as a reliable source of information for human identity recognition due to their universality, portability, and unique and stable biological identification features. This method enriches and refines existing biometric identification techniques, and is suitable for customer identity identification in the insurance industry. This article has the following objectives: 1) to introduce biometric identification techniques commonly used in the insurance industry, including ECG biometric identification techniques, and their advantages; 2) to discuss major aspects of ECG biometric identification techniques; 3) to systematically review the most recent advances in ECG identification and extraction characteristics from research in China and other countries; 4) to outline the technical aspects of using ECGs for recognizing client identity in the insurance industry; and 5) to discuss the future of ECGs in identity recognition. This article is aimed at further promoting the application of living personal ECG identification techniques in the insurance industry, and extending it to areas including medical insurance, banking, justice, public security, military, government, enterprises, and other departments and areas in which identification is needed to promote social stability and national security.

Finger Vein Recognition Based on Anatomical Features of Vein Patterns

Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> EB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.

Class-Imbalance Privacy-Preserving Federated Learning for Decentralized Fault Diagnosis With Biometric Authentication

Privacy protection as a major concern of the industrial big data enabling entities makes the massive safety-critical operation data of a wind turbine unable to exert its great value because of the threat of privacy leakage. How to improve the diagnostic accuracy of decentralized machines without data transfer remains an open issue; especially these machines are almost accompanied by skewed class distribution in the real industries. In this study, a class-imbalanced privacy-preserving federated learning framework for the fault diagnosis of a decentralized wind turbine is proposed. Specifically, a biometric authentication technique is first employed to ensure that only legitimate entities can access private data and defend against malicious attacks. Then, the federated learning with two privacy-enhancing techniques enables high potential privacy and security in low-trust systems. Then, a solely gradient-based self-monitor scheme is integrated to acknowledge the global imbalance information for class-imbalanced fault diagnosis. We leverage a real-world industrial wind turbine dataset to verify the effectiveness of the proposed framework. By comparison with five state-of-the-art approaches and two nonparametric tests, the superiority of the proposed framework in imbalanced classification is ascertained. An ablation study indicates that the proposed framework can maintain high diagnostic performance while enhancing privacy protection.