Score normalization applied to adaptive biometric systems

Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions. The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context. The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition.

Biometric person recognition poses a very challenging pattern recognition problem because of large variability in biometric sample qualityQuality encountered during testing and a restricted number of enrollment samples for training. Furthermore, biometric traits can change over time due to agingAgeing and change of lifestyle. Effectively, the noise factors encountered in testing cannot be represented by the limited training samplesTraining samples . A promising solution to training data deficiency and ageing is to use an adaptive biometric Adaptive biometrics system. These systems attempt to adapt themselves to follow the change in the input biometric data. Adaptive biometricsAdaptive biometrics deserves a treatment on its own right because standard machine-learning algorithms cannot readily handle changing signal quality. The aim of this chapter is to introduce the concept of adaptive biometric systems in terms of taxonomy, level of adaptation, open issues and challenges involved.

The term adaptive biometric systems refers to biometric recognition systems in which an algorithm aimed to follow variations of the clients appearance has been implemented. Among others, the self update algorithm is used when only one biometric is available, and is able to add to the clients gallery novel data collected during system operation, on the basis of a updating threshold: if the novel data, compared with existing template(s), provide a matching score higher than the given threshold, they are added to the gallery. In order to avoid misclassification errors, thus inserting impostors into the clients gallery, this threshold is very conservative. Self-update algorithm has shown to be effective for many biometrics. However, no work tried to explain, so far, why self-update should work, in particular when a very conservative update threshold is used (zeroFAR threshold). This is the goal of the present paper, which provides a conceptual explanation of the self update mechanism coupled with a set of experiments on a publicly available data set explicitly designed for studying adaptive biometric systems.

Online learning environments have become a crucial means to provide flexible and personalised pedagogical material, and a major driving cause is due to the COVID-19 pandemic. This has rapidly forced the migration and implementation of online education strategies across the world. Online learning environments have a requirement for high trust and confidence in establishing a student’s identity and the authenticity of their work, and this need to lessen academic malpractices due to increased online delivery and assure the quality in education has accelerated. In addition to this, due to the ubiquity of mobile devices such as smartphones, tablets and laptops, students use a variety of devices to access online learning environments. Therefore, authentication systems for online learning environments should operate effectively on those devices to authenticate and invigilate online students. Confidence in authentication systems is also crucial to detect cheating and plagiarism for online education as strong authorisation and protection mechanisms for sensitive information and services are bypassed if authentication confidence is low. In this paper, we examine issues of existing authentication solutions for online learning environments and propose a design for an adaptive biometric authentication system for online learning environments that will automatically detect and adapt to changes in the operating environment. Multi-modal biometrics are applied in the proposed system which will dynamically select combinations of biometrics depending on a user’s authenticating device. The adaptation strategy updates two thresholds (decision and adaptation) as well as the user’s biometric template they are using the authentication system.

Background: With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems Methods:: In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called Rest- Net50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature. Results: The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method. Conclusion: The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

This research paper aims to contribute to the field of biometric attendance systems. Biometric system which uses unique biological traits like fingerprints for identification and authentication, offers several benefits over traditional authentication methods like passwords or ID cards. The benefits of using biometric system can be enhanced security, convenience and high efficiency, reduced risk of fraud, cost-effective in long run, accountability, scalability, multi-factor authentication, etc. In most of the organizations or institutes attendance is taken by the traditional methods like register book but this method takes time then attendance from these registers is uploaded to server for further attendance report of students. To withstand this problem, we have proposed an IoT based multipurpose biometric authentication system. We have developed a portable system that includes fingerprint sensors to recognize the fingerprint of various students and show the details of the students whose fingerprints have been scanned. The data from the fingerprint scanner is sent to the Raspberry PI server using Wi-Fi. We also have also designed a user interface for students and faculty to view and manage the students and their attendance. The attendance report can be exported to excel file any time faculty want the report. The biometric system provides 80% efficiency over the regular attendance methods.

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

This paper investigates the potential of gait-based authentication for securing virtual environments, specifically within the Metaverse. With the growing need for reliable and secure identity verification in virtual spaces, traditional authentication methods, such as passwords or PINs, have proven insufficient. In contrast, biometric authentication systems, including gait analysis, provide a more secure and user-friendly alternative by leveraging unique physiological and behavioral traits for identity verification. This research applies machine learning algorithms—Random Forest and Support Vector Machine (SVM)—to gait data for distinguishing between authentic users and imposters. The dataset consists of 1,000 simulated gait samples with 16 features, such as stride length, step frequency, joint angles, and ground reaction forces (GRF). After performing exploratory data analysis (EDA), including feature distribution visualization and correlation analysis, two models were trained on the data. The Random Forest model outperformed the SVM model, achieving an accuracy of 56% and a recall of 76%, indicating its effectiveness in identifying authentic users. Despite the promising results, both models showed only marginal improvement over random guessing, highlighting the need for further optimization. This study contributes to the growing body of research on gait-based biometric systems by demonstrating their potential as a viable method for identity verification in virtual environments. It also identifies the most important gait features, such as step frequency, cadence variability, and knee joint angle, that significantly contribute to the classification process. Future research should explore advanced deep learning techniques and the integration of multimodal biometric systems to enhance the performance and reliability of gait-based authentication.

We would like to outline our opinions about the usability of biometric authentication systems. We outline the position of biometrics in the current field of computer security in the first section of our paper. The second chapter introduces a more systematic view of the process of biometric authentication — a layer model (of the biometric authentication process). The third section discusses the advantages and disadvantages of biometric authentication systems. We also propose a classification of biometric systems that would allow us to compare the biometrics systems reasonably, along similar lines to Common Criteria [1] or FIPS 140–1/2 [4]. We conclude this paper with some suggestions where we would suggest to use biometric systems and where not.

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.

It’s hard to see the modern authentication systems without biometrics technologies. Biometrics systems can be used to access the school and university, for payment confirmation, for access to internet banking. Biometric authentication systems can be based on the face, fingerprint, iris, voice, and other modalities. And all of them have security vulnerabilities. This article is about the attacks on facial biometric systems. It presents the classification of the attacks on facial biometric systems and countermeasures for different types of attacks.

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.

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

A multi-modal biometric authentication system based on iterative fusion operator trees is considered, where the per-user performance is also depending on the order of biometric feature evaluations. Therefore, each user would have its own optimal choice of order parameters of the biometric system. However, for a system approach there should be only one general parametrization of the whole biometric system for all users, and the necessary consequence are performance wins and losses for each user. The common approach for this purpose is to use the parameter setting that maximizes the average performance over users. However, this does not necessarily reflect a good balance of performance wins and losses. In order to gain more insight into the corresponding trade-offs, and to facilitate an effective selection of an order parameter, the concept of fairness relations and their maximality, which has become a recent topic of many investigations in communication network resource sharing tasks, is applied to this problem of multi-modal biometric systems. The central idea of the paper is that by applying fairness relation to select the parameters, we obtain a system where subjects have a more fair chance of being right authenticated. A comparative study of an experimental test data set shows that maxmin fairness gives solutions with major drawbacks for the majority of users, while proportional fairness comes close to the maximum average.