Security Features in Fingerprint Biometric System

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

A linear convolution-based cancelable fingerprint biometric authentication system

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.

To support healthcare professionals in managing the array of information available about patients and the populace, healthcare systems have adopted and continue to incorporate a variety of information and communications technologies (ICT) into the delivery and administration of healthcare. However, isolated ICT systems cannot maximize health outcomes because healthcare delivery occurs within the context of a highly complex system since patients receive healthcare services in a variety of healthcare settings. However, health information exchange (HIE) enables more complete and timely sharing of data and information among ICT systems used in healthcare delivery while supporting provider and patient access to information when and where needed. Thus, this paper presents the development of fingerprint biometric authentication system for health information exchange based the centralized federated HIE model. The proposed HIE system accepts ready-only information from the finger biometric authentication system. The proposed HIE platform has been successfully developed using RESTful API technology and fast health interoperability resource (FHIR) standards to ensure standardized transfer of health information with successful real-time online results. The HIE platform allows for continuous record of patient be made to be available at the point of care subject to authorization by the patient and is applicable in outpatient service delivery, emergency service delivery, public health surveillance, clinical research and clinical trials participants scouting. As a future direction, a clinical decision support system could be implemented on top of the API so as to promote the practice of personalized medicine.

Multimodal Biometrics is an emerging domain in biometric technology where more than one biometric trait is combined to improve the performance. The biometric system take Face, Fingerprint, Voice, Handwritten Signatures, Retina, Iris, Gait, Palm print, Ear & Hand geometry as common features. Human is identified by correct matching of these features. However, features like face, voice, and signature have low permanence and they change with time. Ageing of human, as well as other psychological & environmental conditions cause gradual change in these features. While enrolling feature set we don't consider this factor. Here we propose a new concept that can be used in designing future multimodal biometrics systems which can adapt to the change in the biometrics features like face, voice, signature, and gait over the time or any other factor without compromising the security. Regression based technique can be used to detect change. This algorithm requires use of at least one biometric feature which has very low variance or high degree of permanence, like Fingerprint, Iris, Retina etc. This algorithm can address the problem of false rejection caused by sustained change in biometric features due to Ageing or any other factor without the need of re-enrollment of feature set.

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.

Ambient Intelligence represents the medium with a huge number of connected heterogeneous and dynamic devices that respond to the presence of people. The development and implementation of technologies to ensure the security and reliability of such systems is the main task of many studies in this direction. One of the important technologies for the formation of a secure Ambient Intelligence is biometric systems. Biometric systems represent techniques for describing and measuring personal unique physiological and behavioral characteristics of a person and methods for applying them to automatic identification or authentication processes. In this paper, the basic principles and algorithms of a biometric identification system based on iris recognition are considered. The purpose of the research is to determine the structure of the security systems of Ambient Intelligence using biometric technologies.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Biometrics using biological or behavioral features to authenticate a person has attracted much attention as a new authentication approach against traditional ones such as key, password, etc. Biometrics technologies provide us better security and greater convenience than traditional person authentication technologies such as key, password and card. Biometric systems with cameras involve the risk of spoofing. For example in face recognition systems, when a malicious person turns a printed face photo of an authenticated user to a camera, a face recognition system may accept the malicious person as the authenticated user. To address the above problem, liveness detection is important to develop secure biometric recognition systems. This paper presents a liveness detection method using deep learning for face recognition systems.

This review article's objective is to describe how one of the major advantages of biometric technology, which involves identifying and verifying people by examining their bodily characteristics, in employee attendance helps in better functioning of HR practices. The majority of biometric technology users struggle with the challenge of choosing an precise and cost-effective biometric system in addressing specific issues in a given environment, despite the many benefits of the biometric system and its influence on many job sectors throughout the world. To improve the conventional staff attendance system, which currently has an impact on the organization's efficiency, this article investigates the biometric attendance identifier that may be utilized. Implementing a qualitative (exploratory) technique, the study was conducted. It. is purely exploratory research that provides comprehensive details on biometrics, biometric attendance systems, and their application to Talent Management. Nevertheless, the study demonstrates that a biometric identifier is efficient and cost-effective for the organization's human resources attendance management system as an element of HR procedures, which implies that consideration is given before suggesting the use of biometric technology to improve the effectiveness of business operations in a firm.

Palm print is a unique and reliable biometric characteristic with high usability. Many palm print recognition algorithms and systems have been successfully developed in the past decades. Most of the previous works use the white light sources for illumination. Recently, it has been attracting much research attention on developing new biometric systems with both high accuracy and high anti-spoof capability. Multispectral palm print imaging and recognition can be a potential solution to such systems because it can acquire more discriminative information for personal identity recognition. One crucial step in developing such systems is how to determine the minimal number of spectral bands and select the most representative bands to build the multispectral imaging system. This paper presents preliminary studies on feature band selection by analyzing hyper spectral palm print data (420nm~1100nm). Our experiments showed that 2 spectral bands at 700nm and 960nm could provide most discriminate information of palm print. This finding could be used as the guidance for designing multispectral palm print systems in the future.

A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric data discrimination and multi-biometrics, to enhance the recognition performance of biometric systems. In Section 1.1, we discuss the necessity, importance, and applications of biometric recognition technology. A brief introduction of main biometric recognition technologies are presented in Section 1.2. In Section 1.3, we describe two advanced biometric recognition technologies, biometric data discrimination and multi-biometric technologies. Section 1.4 outlines the history of related work and highlights the content of each chapter of this book.

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.

The aim of this chapter is to introduce biometrics systems and discuss the essential components of biometrics technologies in the healthcare system. The discussion also includes the state-of-the-art biometrics technologies, selection criteria of a suitable biometrics system, biometrics identity management, and multi-biometrics fusion for healthcare biometrics system.

Biometric authentication methods are being increasingly used for many types of applications. Since such methods necessitate humans to interact with a device, effective implementation requires consideration of the perceptions and responses of end users. Towards this goal, we present in this paper a modality-independent evaluation methodology to study users' acceptance and satisfaction of biometric systems. It is based on the use of a questionnaire and some data mining tools for the analysis. We have applied it on two biometric systems developed in our research laboratory. The results from this study demonstrated that users' satisfaction analysis should be more taken into account when developing biometric systems. A significant panel of 70 users was more satisfied from the keystroke system than the other one. Users surprisingly considered that its perceived performance was also better even if the used face system has a better performance with an EER of 8.76% than the keystroke one with an EER of 17.51%. The robustness of a system against attacks and its perceived trust have been identified as important factors to take into account when designing biometric systems. Results have also demonstrated significant relationships between demographic factors and their perception about the biometric technology and the studied systems.