Biometric-based Authentication Systems Research Articles

Biometric ATM Authentication System

As technology progresses, traditional authentication methods like Personal Identification Numbers (PINs) and passwords are becoming more prone to security breaches. To address this issue, biometric-based authentication systems are being seen as a reliable solution to boost security in different areas, such as Automated Teller Machines (ATMs). This study delves into the deployment, advantages, obstacles, and future potential of a biometric-based ATM system. By examining current research, real-life examples, and technological innovations, the aim of this paper is to shed light on the efficiency and viability of biometric authentication in ATM systems. Key Words: Enhanced Security, Authentication, Efficiency, Reliability

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Biometric-Driven Digital Village Governance: A SWOC-Enabled Model with Fraud Prevention

In the digital age, the integration of advanced technologies and biometrics plays a pivotal role in transforming governance models. This research introduces an innovative approach to digital village governance, leveraging the potential of biometric data in biomedical applications while incorporating robust fraud detection mechanisms. The proposed model harnesses the power of Swarm Fish Optimization Classification (SWOC) to enhance the accuracy and efficiency of biometric-based authentication systems, ensuring the security of village governance processes. The study outlines the multifaceted components of the Intelligent Digital Village Governance Model, emphasizing its adaptability to the unique needs of rural communities. Central to this model is the utilization of biometric data, such as fingerprints, for user identification, access control, and the delivery of essential services. SWOC, with an optimization and classification algorithm inspired by swarm behavior, is integrated to refine the accuracy of biometric identification and detect fraudulent activities. Experimental results demonstrate the efficacy of SWOC in enhancing the accuracy of biometric-based authentication, thereby strengthening the security of digital village governance. The model's adaptability, scalability, and compliance with ethical standards are also discussed, ensuring responsible deployment in rural settings.

OCA: An intelligent model for improving security breach of biometric based authentication systems

In the recent era, biometrics is one of the most used authentication systems. Generally, biometric systems are based on imaging technology, which works on scanned images. This system processes the image and compares it with the database and decides authenticity of user. Authentication and faster response are the key objectives of all biometric systems, but they suffered from a serious issue i.e., security breach. In this article, a Cellular Automata based method is introduced, which processes the image and identify possible security breaches. The proposed method analysis all the pixels of the image and maps a rule, which processes the image and generates a gray image by defining all the possible edges of the image objects. The gray image contains only binary values, which may reduce the chance of breach and decrease the comparison time of gray image with database. In this paper, the proposed method is experimented on the open-source dataset and the results are validated using authentication parameters.

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation.

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

Biometric based authentication systems are being prominently used everywhere. The biometric data, popularly known as a biometric template, is generally stored on the database server in its unprotected form. Unlike passwords, once compromised, biometric data can never be recovered. An ideal biometric system should provide accessibility, acceptability, availability, high security, and high biometric performance to the user. Current deployments generally relax in one or more requirements, resulting in lingering concerns about the privacy and security of individuals’ biometric data. Our proposed work introduces a cancelable biometric template protection scheme based on the format-preserving encryption and Bloom filters. The format-preserving encryption encrypts the biometric template, which then maps to the Bloom filter based template that represents the cancelable template. The use of format-preserving encryption along with Bloom filters helps to achieve the security of the input biometric template and identification with good recognition performance. We achieve 0.2% FRR at 0.01% FAR for IITD-CASIA virtual dataset in the uni-biometric scenario. A comparison with the existing schemes shows that our proposed scheme exhibits high recognition performance for both uni-biometric and multi-biometric datasets, while simultaneously, the security of the overall system is preserved.

Biometric Based Authentication System for Computer Based Assessment

Electronic-Learning has attracted a lot of attention in recent years. This is evident by the increase in population shift from the traditional way of learning. An important aspect of Electronic Learning is the assessment of students. Since the traditional way of student assessment is characterized by issues such as insecurity, they are replaced by Electronic means of assessment. Most Electronic assessment systems make use of the knowledge based and object based methods for authentication, which can be compromised,shared or transferred, resulting into issues such as impersonation.This research focuses on developing a system that uses facial biometric traits in authenticating candidtates for Electrronic Examination.The research uses some mathematical models based on the Principal Component Analysis(PCA) technique for face recognition.The face is prefered for its ability to offer continous authentication,which will eradicate isues such as impersonation. The system was tested in the department of Mathematics and Computer Science, University of Mkar, Mkar, Nigeria and it offers a reliable platform for authentication.

Biometric iris templates security based on secret image sharing and chaotic maps

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template.

Finger Vein Authentication Based on Wavelet Scattering Networks

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades. Multiple biometrics such as fingerprint, palm, iris, palm vein and finger vein and other biometrics have been introduced. One of the challenges in biometrics is physical injury. Biometric of finger vein is of the biometrics least exposed to physical damage. Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate. This paper presents a novel method of scattering wavelet-based identity identification. Scattering wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural networks. What distinguishes this algorithm from other popular feature extraction methods such as deep learning methods, filter-based methods, statistical methods, etc., is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes, even when the image is subject to serious damage such as noise, angle changes or pixel location, this descriptor still generates feature vectors in a way that minimizes classifier error. This improves classification and authentication. The proposed method has been evaluated using two databases Finger Vein USM (FV-USM) and Homologous Multi-modal biometrics Traits (SDUMLA-HMT). In addition to having reasonable computational complexity, it has recorded excellent identification rates in noise, rotation, and transmission challenges. At best, it has a 98.2% identification rate for the SDUMLA-HMT database and a 96.1% identification rate for the FV-USM database.

Secure User Verification and Continuous Authentication Via Earphone IMU

Biometric plays an important role in user authentication. However, the most widely used biometrics, such as facial feature and fingerprint, are easy to capture or record, and thus vulnerable to spoofing attacks. On the contrary, intracorporal biometrics, such as electrocardiography and electroencephalography, are hard to collect, and hence more secure for authentication. Unfortunately, adopting them is not user-friendly due to their complicated collection methods or inconvenient constraints on users. In this paper, we propose a novel biometric-based authentication system, namely <i>MandiPass</i>. <i>MandiPass</i> leverages inertial measurement units, which have been widely deployed in portable devices, to collect intracorporal biometric from the vibration of user&#x0027;s mandible. It provides not only one-time verification function but also continuous authentication function. Both the two functions are secure and user-friendly. We theoretically validate the feasibility of <i>MandiPass</i> and develop a series of deep learning techniques for effective biometric extraction. We also utilize a Gaussian matrix to defend against replay attacks. Extensive experiment results with 34 volunteers show that <i>MandiPass</i> can achieve low equal error rate, even under various harsh environments.

Progression in Biometric Recognition Systems and its Security

: The security of information is the major problem faced in today’s digital environment. Biometric Systems helps in achieving high–end security at precise performance requirements for various applications. This paper surveys the present multimodal and unimodal biometric based authentication systems which are in use, and their future possibilities. The authentication techniques that can identify the individual using unique features of the human such as face, fingerprint, DNA, speech and iris are discussed. Different recognition systems for new biometric traits such as voice, gait, and lip moment are also focused. This paper also focuses on information security of biometric systems and their performance requirements.

PSEBVC: Provably Secure ECC and Biometric Based Authentication Framework Using Smartphone for Vehicular Cloud Environment

The Vehicular Cloud Environment (VCE) is a brand-new study field in cloud and vehicular network.It gives cars networking and sensor capabilities for V2I or V2V communication with roadside infrastructure. Cloud applications are frequently used in traffic control and road safety. A hybrid technical solution that utilises vehicle resources, cloud infrastructure, and Internet of Things (IoT) settings is needed for effective vehicular communication networking. VCE is a smart vehicular communication architecture that promotes system security, enhanced vehicle control, and self-driving cars. Due to the integration of unknown vehicles and infrastructure via the public network, security and privacy seem to be significant challenges with VCE. In this regard, we propose a PSEBVC, which is a provably secure elliptic curve cryptography (ECC) and biometric based authentication system for VCE employing smartphones. In the face of active and passive adversaries, the offered framework obtains the majority of security features and properties for secure communication. We also propose and prove a formal security model based on the random oracle concept. We also demonstrate the security analysis using the Scyther tool. In the same scenario, we evaluate the performance of our protocol against that of other frameworks. The proposed system, according to our findings, is both secure and efficient in terms of communication and processing overhead. The proposed architecture, according to our findings, provides all needed security criteria while also permitting effective communication.

Implementation of Efficient Biometric-Based Access for Secured Cloud Services

In our data-driven culture, the need for remote data storage and computing services has grown exponentially, requiring secure access to such data and services. This study proposes a new biometric-based authentication system for secure access to remote (cloud) servers. The proposed approach treats the user's biometrics as confidential credentials. It then uses the user’s biometric data to generate a unique ID and uses it to generate the user's private key. In addition, we present an efficient way to generate session keys for secure messaging between two interlocutors using two biometric templates. That is, you don't have to store your private and session keys somewhere. The proposed approach follows formal security analysis using detailed RealorRandom (ROR) model-based formal security analysis, informal (unmathematical) security analysis, and widely accepted automated Internet security verification., Can withstand multiple known attacks against (passive / active) attackers. Protocol and application (AVISPA) tools. Finally, numerous and comparative studies have shown the effectiveness and usefulness of the proposed approach.

ChestLive

Voice-based authentication is prevalent on smart devices to verify the legitimacy of users, but is vulnerable to replay attacks. In this paper, we propose to leverage the distinctive chest motions during speaking to establish a secure multi-factor authentication system, named ChestLive. Compared with other biometric-based authentication systems, ChestLive does not require users to remember any complicated information (e.g., hand gestures, doodles) and the working distance is much longer (30cm). We use acoustic sensing to monitor chest motions with a built-in speaker and microphone on smartphones. To obtain fine-grained chest motion signals during speaking for reliable user authentication, we derive Channel Energy (CE) of acoustic signals to capture the chest movement, and then remove the static and non-static interference from the aggregated CE signals. Representative features are extracted from the correlation between voice signal and corresponding chest motion signal. Unlike learning-based image or speech recognition models with millions of available training samples, our system needs to deal with a limited number of samples from legitimate users during enrollment. To address this problem, we resort to meta-learning, which initializes a general model with good generalization property that can be quickly fine-tuned to identify a new user. We implement ChestLive as an application and evaluate its performance in the wild with 61 volunteers using their smartphones. Experiment results show that ChestLive achieves an authentication accuracy of 98.31% and less than 2% of false accept rate against replay attacks and impersonation attacks. We also validate that ChestLive is robust to various factors, including training set size, distance, angle, posture, phone models, and environment noises.

Biometrics for Internet-of-Things Security: A Review.

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

Comparison of levels and fusion approaches for multimodal biometrics

The biometric-based authentication system occupies maximal space in the field of security administration. Biometric applications are swiftly accelerating in day-to-day life such as computer login, smart homes, online banking, hospitals, border areas, industries, forensics, e-voting attendance system and investigation of crime. A reliable and accurate recognition body can be achieved with multimodal biometric methodologies. In this paper, we discuss starting with an introduction to biometric systems followed by their classification, and advantages as well as disadvantages. In today’s world, most of the systems are unimodal biometrics having a lot of limitations to overcome those multimodal biometrics comes in to picture. In this paper we have discussed comprehensive representation on the system of multimodal biometric, various modes of undertakings, the significance of information fusion, a different section is allotted on the various possible levels of fusion involving sensor-level, feature-level, score-level, and decision -level as well as different rules of fusion.

Accurate feature extraction for multimodal biometrics combining iris and palmprint

Multimodal biometric systems provide a way to combat with the limitations of a unimodal biometric system which include less accuracy and user acceptability. In this context, a coding based approach called bit-transition code, is proposed for addressing the less-explored problem of designing a biometric-based authentication system by combining the iris and palmprint modalities. The approach is based on the encoding of binary transitions of symmetric and asymmetric parts of the Gabor filtered images at all pixel locations. Score-level fusion is employed to integrate the individual iris and palmprint performances. Experiments are carried out with three benchmark iris/palmprint databases, namely IITD iris and palmprint databases and PolyU palmprint database. The performance is measured in terms of receiver operator characteristics (ROC) curves and other metrics, like equal error rate and area under ROC curves. A comprehensive comparison, with several state-of-the-art approaches, is presented in order to validate the usefulness of the proposed approach.

Energy‐efficient multi‐biometric system for Internet of Things using trust management

The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multi-biometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multi-biometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.

Privacy preserving biometric authentication using Chaos on remote untrusted server

Biometric-based authentication systems are promising because of unique biometric features for each person. Generally, biometric features are generated , and biometric templates or bio-hash codes are calculated and stored in Authentication Data Table (ADT) along with userid. However, such hashing based authentication is vulnerable to precomputed table attacks. Also, in some cases, the service providers outsource the ADT to cloud or untrusted server. In this paper, a novel biometric-based authentication system is proposed using two servers: crypto-match server and untrusted storage server. The system utilizes biometric image cryptosystem, cryptographic hashing, and Paillier cryptosystem. In the proposed cryptosystem, keystreams are generated from Henon and logistic maps. The control parameters of these chaotic maps are calculated from the input biometric image. The proposed biometric encryption is able to resist statistical and differential attacks and its security level is also validated through various analyzes. Also, the attacks related to the privacy of ADT are analyzed.

Random area-perimeter method for generation of unimodal and multimodal cancelable biometric templates

In recent times, biometric based authentication systems have seen a tremendous growth in various applications. However, if databases in multiple applications are created using the same biometric characteristic and algorithm, then any compromise of the stored template in one biometric system may jeopardise the security of the other biometric systems as well. More importantly, such a compromise may also lead to a permanent loss of the biometric characteristic. Therefore, the cancelability or revocability of biometrics has become quite an essential requirement. In this paper, a novel scheme, the Random Area & Perimeter Method (RAPM)) is presented in which a biometric characteristic of an individual is transformed into random values which are stored as cancelable biometric templates. The proposed scheme computes area and perimeter of the Bezier curve which are obtained through interpolation of feature points of original biometrics and a random point chosen by the user. The area and perimeter thus computed exhibit pseudo-random properties. This technique outperforms many existing techniques. Moreover, a dimensionality reduction to the tune of more than 95% has been obtained without compromising the matching performance. The average values obtained for EER, DI and RI are 0.0045, 6.28 and 99.64 respectively. These values are better than those obtained from the available state of the art approaches. The proposed scheme has also been analysed for various privacy issues like non-invertibility, revocability, and unlinkability.