Biometric Authentication

A breakthrough approach to improving biometrics performanceConstructing robust information processing systems for face and voice recognitionSupporting high-performance data fusion in multimodal systemsAlgorithms, implementation techniques, and application examples Machine learning: driving significant improvements in biometric performance As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems. Coverage includes: How machine learning approaches differ from conventional template matchingTheoretical pillars of machine learning for complex pattern recognition and classificationExpectation-maximization (EM) algorithms and support vector machines (SVM)Multi-layer learning models and back-propagation (BP) algorithmsProbabilistic decision-based neural networks (PDNNs) for face biometricsFlexible structural frameworks for incorporating machine learning subsystems in biometric applicationsHierarchical mixture of experts and inter-class learning strategies based on class-based modular networksMulti-cue data fusion techniques that integrate face and voice recognitionApplication case studies

Biometric user authentication techniques evoke an enormous interest by science, industry and society. Scientists and developers constantly pursue technology for automated determination or confirmation of the identity of subjects based on measurements of physiological or behavioral traits of humans. Biometric User Authentication for IT Security: From Fundamentals to Handwriting conveys general principals of passive (physiological traits such as fingerprint, iris, face) and active (learned and trained behavior such as voice, handwriting and gait) biometric recognition techniques to the reader. Unlike other publications in this area that concentrate on passive schemes, this professional book reflects a more comprehensive analysis of one particular active biometric technique: handwriting. Aspects that are thoroughly discussed include sensor characteristic dependency, attack scenarios, and the generation of cryptographic keys from handwriting.

This book constitutes the refereed proceedings of the First International Conference on Biometric Authentication, ICBA 2004, held in Hong Kong, China in July 2004. The 104 revised full papers presented were carefully reviewed and selected from 157 submissions; also included are summaries of 3 biometric competitions on fingerprint verification, face authentication, and signature verification. The papers are organized in topical sections on face, fingerprint, iris, signature, speech, biometric fusion and risk analysis, and other biometric issues.

This book constitutes the refereed proceedings of the International Biometric Authentication Workshop, BioAW 2004, held in Prague, Czech Republic, in May 2004, as part of ECCV 2004. The 30 revised full papers presented were carefully reviewed and selected for presentation. The papers are organized in topical sections on face recognition, fingerprint recognition, template protection and security, other biometrics, and fusion and multimodal bioinformatics.

This book offers an overview of the field of continuous biometric authentication systems, which capture and continuously authenticate biometrics from user devices. This book first covers the traditional methods of user authentication and discusses how such techniques have become cumbersome in the world of mobile devices and short usage sessions. The concept of continuous biometric authentication systems is introduced and their construction is discussed. The different biometrics that these systems may utilise (e.g.: touchscreen-gesture interactions) are described and relevant studies surveyed. It also surveys important considerations and challenges. This book brings together a wide variety of key motivations, components and advantages of continuous biometric authentication systems. The overview is kept high level, so as not to limit the scope to any single device, biometric trait, use-case, or scenario. Therefore, the contents of this book are applicable to devices ranging from smartphones to desktop computers, utilising biometrics ranging from face recognition to keystroke dynamics. It also provides metrics from a variety of existing systems such that users can identify the advantages and disadvantages of different approaches. This book targets researchers and lecturers working in authentication, as well as advanced-level students in computer science interested in this field. The book will also be of interest to technical professionals working in cyber security.

This book constitutes the proceedings of the First International Workshop on Biometric Authentication, BIOMET 2014, which was held in Sofia, Bulgaria, in June 2014. The 16 full papers presented in this volume were carefully reviewed and selected from 21 submissions. Additionally, this volume also contains 5 invited papers. The papers cover a range of topics in the field gait and behaviour analysis; iris analysis; speech recognition; 3D ear recognition; face and facial attributes analysis; handwriting and signature recognition; and multimodal and soft biometrics.

Student assessment in online learning is submitted remotely without any face-to-face interaction, and therefore, student authentication is widely seen as one of the major challenges in online examination. Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. As the dependence upon computers and computer networks grows, especially within education, the need for authentication has increased. Biometric Authentication in Online Learning Environments provides innovative insights into biometrics as a strategy to mitigate risk and provide authentication, while introducing a framework that provides security to improve e-learning and on-line examination by utilizing biometric-based authentication techniques. This book examines e-learning, security, threats in online exams, security considerations, and biometric technologies, and is designed for IT professionals, higher education administrators, professors, researchers, business professionals, academicians, and libraries seeking topics centered on biometrics as an authentication strategy within educational environments.

Biometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.