Biometric systems, modalities and attacks

There is insufficient consideration of Generation Z’s cultural and generational needs in the implementation of biometric attendance systems in Arabic educational settings. This study delves into Generation Z’s discipline, exploring their perspectives on attendance systems and aligning commitment with their interests. The primary aim is to gauge biometric systems’ impact on productivity. Google Form questionnaires collected data from young employees, ages 25 to 35, who belong to Generation Z’s working in the higher education system. Structural equation modeling and descriptive analysis assessed the data. While biometric systems enhance discipline, they may dampen morale. Implementing systems fairly and maintaining flexibility is vital. The study underscores the importance of evaluating employees based on achievements. It sheds light on biometric systems’ role in attendance management and organizational performance, aiding HR practices. The results showed no significant effect of Employee Management Practices (EMP) on organization performance through Biometric Attendance Technology (BAT) (B = 0.049, t = 1.330, p = 0.184). Nor significant effects of Organizational Performance Metrics (OPM) (B = 0.019, t = 0.608, p = 0.543). Technological Infrastructure (TI) (B = 0.019, t = 0.2461, p = 0.645), or Satisfaction and Engagement (ESE) (B = 0.057, t = 1.381, p = 0.167) on organization performance through Biometric Attendance Technology. The mediator impact was also found to be not significant (P > 0.05). Therefore, both direct and specific indirect effects were not significant. Indicating that Biometric Attendance Technology does not mediate the relationship between these variables and organizational performance.

This research discusses the evaluation of biometric systems that are designed to process hundreds to tens of thousands of individuals in short time spans. We propose a method for evaluating a system's performance across capture attempts for the purpose of identifying characteristics that are advantageous in these high-throughput environments. We also present a novel modification to the traditionally accepted biometric performance metrics of failure-to-acquire, and true-match rate. Namely, this paradigm shift holds that these metrics are a function of time and, as such, vary with the time available for a biometric system to interact with a user. This research demonstrates the utility of these time-based metrics in evaluating the performance of multiple, commercially available, high-throughput systems. We show that different biometric systems have notably different time-based performance curves using a corpus of data collected during the 2018 Department of Homeland Security, Science and Technology Directorate (DHS S&T) Biometric Technology Rally. These curves and the deviations between them are useful when quantifying the suitability of a technology, evaluated via scenario testing, for deployment in an operational environment where the throughput of the target population is a key performance parameter.

Traditional authentication methods such as passwords are susceptible to easy hacking, and as technology progresses, the demand is on the rise for more reliable and secure recognition systems, which can be used in surveillance and biometrics applications etc. A biometric recognition system of individuals is established on the unique features of the individual. Multi modal biometric systems represent a significant research field with widespread applications of recognition systems. Unimodal biometric systems have a variety of issues, including nonuniversality and noisy data. Certain limitations and difficulties can be partially addressed with the use of multi modal biometric systems. In this paper, a survey on multi-biometric systems is presented to highlight the challenges, strengths and weaknesses of some of the research discussed in this study which contributes to enhancing the understanding and development of robust and reliable biometric authentication solutions, essential for ensuring security in various domains as well as suggest directions for future work. Index Terms— biometric recognition, multimodal biometric, biometric system, fusion techniques, multi-biometric system.

It is an undeniable fact that biometric recognition systems are susceptible to different software-related or template attacks at different vulnerable points in the biometric authentication system. However, these attacks are frequently overlooked, implying one of two possibilities: either researchers and biometric stakeholders have forgotten or simply ignored the consequences of these attacks, or they are unaware of some of these threats. Meanwhile, if biometric systems do not have the required protection, these attacks can lead to biometric system compromise and consequently the loss of sensitive personal data, which is irreplaceable and irrevocable. Therefore, considering the significance of the implications posed by these threats, researchers and biometric stakeholders have, over the last two decades, proposed different methods to mitigate some of the attacks, while a large number of the existing attacks are left unattended to. Hence, we deemed it paramount to bring this to the attention of the research community. This article is an effort to emphasize the implications of these attacks, exploit the threat approach based on attackers’ knowledge, and consolidate them into a single, thorough, and comprehensive review together with recent state-of-the-art protection mechanisms that are put forward to mitigate these threats. Moreover, a comparative performance analysis of these protection mechanisms and other possible countermeasures to the attacks is given. It is expected that this survey will, on the one hand, be a wakeup call to the biometric template protection research community, thus drawing their attention to the task at hand. On the other hand, an awareness to the ordinary users and biometric stakeholders on the risks posed by these threats.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

The field of Deep Learning (DL) and Machine Learning (ML) has evolved over the years, these models generally consist of biometric recognition systems which need an extensive dataset, high-performance computing systems, and a time- consuming process approach. With existence of modern frameworks such as Generative Adversarial Networks, numerous features in biometric systems which are employed for data security can be spoofed into, as a result it creates unexplored vulnerability. These models are extremely sophisticated and are now able to manufacture artificial human features which are difficult to differentiate from the authentic ones. The work in this paper helps to recognize and explore various GAN-synthesized corneal features with the use of style generative adversarial network 2 (StyleGAN-2). The model presented extract features of eyes from the facial images passed into it, Specifically, the cornea which consists of specular highlights of an individual. The training method used for the model is employed by generating multiple unique data instances, which synthesize enormous high quality dataset containing features resembling the training data. Thus, simplifying the process of training the model in an efficient manner by making use of progressive generators in StyleGAN-2 with CNN algorithms. Generated images and real images are further provided to the discriminator for discerning the model-synthesized eyes from real eyes. The generation of synthesized images, to the extraction of corneal specular highlights was experimented using three different processing methods: Central Processing Unit (CPU), Graphical Processing Unit (GPU) and a Tensor Processing Unit (TPU). Comparative analysis on these three methods was performed based on performance and time taken when tested with varying quantity of datasets and diverse environment.

The ability to recognize people uniquely and to associate personal attributes such as name and nationality with them has been very important to the fabric of human society. Nowadays, modern societies have an explosion in population growth and increased mobility which necessitated building advanced identity management systems for recording and maintaining people’s identities. In the last decades, biometrics has played an important role in recognizing people instead of traditional ways such as passwords and keys which can be forgotten or be stolen. Biometric systems employ physiological and/or behavioral characteristics of people to verify their identities. There are different biometric modalities that can be used to recognize people such as fingerprints, face, hand geometry, voice, iris, signature, etc. In this paper, a comprehensive overview have been provided on the major issues of biometric systems including general biometric system architecture, major biometric traits, biometric systems performance, and some relevant works. http://dx.doi.org/10.25130/tjps.24.2019.120

Summary form only given. Biometrics refers to the automatic identification (or verification) of an individual (or a claimed identity) by using certain physical or behavioral traits associated with the person. By using biometrics it is possible to establish an identity based on `who you are¿, rather than by `what you possess¿ (e.g., an ID card) or `what you remember¿ (e.g., a password). Therefore, biometric systems use fingerprints, hand geometry, iris, retina, face, vasculature patterns, signature, gait, palmprint, or voiceprint to determine a person¿s identity. The purpose of this tutorial is two-fold: (a) to introduce the fundamentals of biometric technology from a pattern recognition and signal processing perspective by discussing some of the prominent techniques used in the field; and (b) to convey the recent advances made in this field especially in the context of security, privacy and forensics. To this end, the design of a biometric system will be discussed from the viewpoint of four commonly used biometric modalities - fingerprint, face, hand, and iris. Various algorithms that have been developed for processing these modalities will be presented. Methods to protect the biometric templates of enrolled users will also be outlined. In particular, the possibility of performing biometric matching in the cryptographic domain will be discussed. The tutorial will also introduce concepts in biometric fusion (i.e., multibiometrics) in which multiple sources of biometric information are consolidated. Finally, there will be a discussion on some of the challenges encountered by biometric systems when operating in a real-world environment and some of the methods used to address these challenges.

Biometric systems have been actively emerging in various industries in the past few years and continue to provide higher-security features for access control systems. Many types of unimodal biometric systems have been developed. However, these systems are only capable of providing low- to mid-range security features. Thus, for higher-security features, the combination of two or more unimodal biometrics (multiple modalities) is required. In this paper, we propose a multimodal biometric system for person recognition using face, fingerprint, and finger vein images. Addressing this problem, we propose an efficient matching algorithm that is based on secondary calculation of the Fisher vector and uses three biometric modalities: face, fingerprint, and finger vein. The three modalities are combined and fusion is performed at the feature level. Furthermore, based on the method of feature fusion, the paper studies the fake feature which appears in the practical scene. The liveness detection is append to the system, detect the picture is real or fake based on DCT, then remove the fake picture to reduce the influence of accuracy rate, and increase the robust of system. The experimental results showed that the designed framework can achieve an excellent recognition rate and provide higher security than a unimodal biometric-based system, which are very important for a IoMT platform.

Biometric authentication systems offer a secure and convenient method of verifying the identity of individuals based on their unique physiological or behavioral traits. This paper presents a comprehensive survey of biometric authentication systems, focusing on the principles, technologies, applications, challenges, and future directions in the field. The survey covers various biometric modalities, including fingerprint recognition, iris recognition, facial recognition, keystroke dynamics, voice recognition, and gait analysis. Key topics discussed include the use of multi-modal biometric systems, continuous authentication, machine learning, and AI in biometrics, and biometric encryption. The paper also explores the implementation of biometric authentication in access control systems, financial transactions, healthcare systems, and government and law enforcement. Challenges such as security and privacy concerns, spoofing and impersonation, scalability and integration, and ethical and legal issues are addressed. Future research opportunities include improving accuracy and reliability, enhancing security and privacy, addressing societal and ethical concerns, and integrating biometric authentication with the Internet of Things (IoT).

In biometric systems, people may be asked to provide multiple scans for redundancy and quality control. In the case of fingerprint matching systems, repeat fingerprint probes of the same physical finger can be available and data from such multiple samples can be fused for reliable authentication of individuals. Since multiple samples are from the same instance of the finger, some relationships between them, e.g. diversity or similarity, could be observed. In this paper, we investigate such relationships and use them in fusion in order to improve the performance of biometric systems. The relationships between samples are derived by measuring the similarity between matching score vectors with Pearson's correlation and cosine similarity measures. We conduct experiments using the FVC2002 dataset consisting of four fingerprint databases and trainable combination methods, likelihood ratio and multilayer perceptron. The results show that utilization of similarity measures for matching scores can further improve the multi-sample biometric fusion in both combination methods.

In the case of most of the biometric recognition systems, the type of features extracted from the biometric template depends on the biometric modality. A generalized feature representation is convenient and efficient in multimodal biometric system as the complexity involved in fusing or protecting the heterogeneous features corresponding to different biometric modalities can be avoided. The dimensionality of the features can also be reduced effectively if we can extract generalized feature vectors without compromising the matching performance. This work proposes two schemes: (i) a generalized yet efficient matching technique based on the cosine similarity of the principal component subspaces (ii) a protection scheme by fusing the feature vectors of multiple modalities in principal components domain and matching in that protected domain. The key feature of the proposed protection scheme over existing schemes is that it does not require a user specific key for ensuring the irreversibility. User specific key is used only for ensuring the revocability of the protected template. Results show that the proposed method is able to achieve a matching performance of $$98.57\%$$ for a multimodal system with face, fingerprint, and finger vein templates in the fused domain.Ragendhu, S. P. Thomas, Tony

Biometric systems use personal biological or behavioural traits that can uniquely characterise an individual but this uniqueness property also becomes its potential weakness when the template characterising a biometric trait is stolen or compromised. To this end, we consider two strategies to improving biometric template protection and performance, namely, (1) using multiple privacy schemes and (2) using multiple matching algorithms. While multiple privacy schemes can improve the security of a biometric system by protecting its template; using multiple matching algorithms or similarly, multiple biometric traits along with their respective matching algorithms, can improve the system performance due to reduced intra-class variability. The above two strategies lead to a novel, ensemble system that is derived from multiple privacy schemes. Our findings suggest that, under the worst-case scenario evaluation where the key or keys protecting the template are stolen, multi-privacy protection scheme can outperform a single protection scheme as well as the baseline biometric system without template protection.

Gait is a relatively biometric modality which has a precious advantage over other modalities, such as iris and voice, in that it can be easily captured from a distance. Although it has recently become a topic of great interest in biometric research, there has been little investigation into gait spoofing attacks where a person tries to imitate the clothing or walking style of someone else. We recently analyzed for the first time the effects of spoofing attacks on silhouette-based gait biometric systems and showed that it was indeed possible to spoof gait biometric systems by clothing impersonation and the deliberate selection of a target that has a similar build to the attacker. To gain deeper insight into the performance of current gait biometric systems under spoofing attacks, we provide a thorough investigation on how clothing can be used to spoof a target and evaluate the performance of two state-of-the-art recognition methods on a gait spoofing database recorded at the University of Southampton. Furthermore, we describe and evaluate an initial solution coping with gait spoofing attacks. The obtained results are very promising and point out interesting findings which can be used for future investigations.

Biometric authentication is the process of recognizing a person by means of his\her psychological or behavioral traits. One of the most important issues faced by the biometric system developer is to protect the template obtained from the biometric of a person. Unimodal biometric system has some drawbacks such as noisy data, interclass variations and spoof attack. Multimodal biometric system has been developed to address the boundaries of unimodal biometric system and increase the security of template. In this paper, template security analysis of multimodal biometric system based of fingerprint and palmprint is proposed and implemented. Fuzzy vault scheme is employed to protect both the fingerprint and palmprint template. At enrollment, image processing techniques such as image enhancement, segmentation and bottom-hat filtering are applied on both the biometric to improve the quality and subsequently the most important features are extracted. Extracted features are concatenated. Combined features along with secret key are utilized to generate the database in the vault. During authentication, query images are sent as an input with the stored template to recover the key. Experimental results are shown that the proposed multi biometrics system performs well than the other methods considered for comparison.