This book examines the proliferation of surveillance technologies&—such as facial recognition software and digital fingerprinting&—that have come to pervade our everyday lives. Often developed as methods to ensure "national security," these technologies are also routinely employed to regulate our personal information, our work lives, what we buy, and how we live.

Biometric recognition-the automated recognition of individuals based on their behavioral and biological characteristic-is promoted as a way to help identify terrorists, provide better control of access to physical facilities and financial accounts, and increase the efficiency of access to services and their utilization. Biometric recognition has been applied to identification of criminals, patient tracking in medical informatics, and the personalization of social services, among other things. In spite of substantial effort, however, there remain unresolved questions about the effectiveness and management of systems for biometric recognition, as well as the appropriateness and societal impact of their use. Moreover, the general public has been exposed to biometrics largely as high-technology gadgets in spy thrillers or as fear-instilling instruments of state or corporate surveillance in speculative fiction. Now, as biometric technologies appear poised for broader use, increased concerns about national security and the tracking of individuals as they cross borders have caused passports, visas, and border-crossing records to be linked to biometric data. A focus on fighting insurgencies and terrorism has led to the military deployment of biometric tools to enable recognition of individuals as friend or foe. Commercially, finger-imaging sensors, whose cost and physical size have been reduced, now appear on many laptop personal computers, handheld devices, mobile phones, and other consumer devices. Biometric Recognition: Challenges and Opportunities addresses the issues surrounding broader implementation of this technology, making two main points: first, biometric recognition systems are incredibly complex, and need to be addressed as such. Second, biometric recognition is an inherently probabilistic endeavor. Consequently, even when the technology and the system in which it is embedded are behaving as designed, there is inevitable uncertainty and risk of error. This book elaborates on these themes in detail to provide policy makers, developers, and researchers a comprehensive assessment of biometric recognition that examines current capabilities, future possibilities, and the role of government in technology and system development.

Reid (senior product manager, Cryptometrics) introduces the technical capabilities and limitations of computer biometric systems for measuring fingerprints, eye characteristics, or other body information as a computer security measure serving a similar purpose to personal identification numbers. He describes the workings of the different types of technologies and examines some of the mathematics behind biometric systems. He also describes the conceptualization and implementation of a particular system with which he was involved. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).

Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc. In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance. Covers different machine intelligence concepts, algorithms and applications in the field of cybersecurity, e-health monitoring, secure cloud computing and secure IOT based operations Explores advanced approaches to improve recognition performance of biometric systems with the use of recent machine intelligence techniques Introduces detection or segmentation techniques to detect biometric characteristics from the background in the input sample

While a sharp debate is emerging about whether conventional biometric technology offers society any significant advantages over other forms of identification, and whether it constitutes a threat to privacy, technology is rapidly progressing. Politicians and the public are still discussing fingerprinting and iris scan, while scientists and engineers are already testing futuristic solutions. Second generation biometrics - which include multimodal biometrics, behavioural biometrics, dynamic face recognition, EEG and ECG biometrics, remote iris recognition, and other, still more astonishing, applications – is a reality which promises to overturn any current ethical standard about human identification. Robots which recognise their masters, CCTV which detects intentions, voice responders which analyse emotions: these are only a few applications in progress to be developed. This book is the first ever published on ethical, social and privacy implications of second generation biometrics. Authors include both distinguished scientists in the biometric field and prominent ethical, privacy and social scholars. This makes this book an invaluable tool for policy makers, technologists, social scientists, privacy authorities involved in biometric policy setting. Moreover it is a precious instrument to update scholars from different disciplines who are interested in biometrics and its wider social, ethical and political implications.

Biometric Systems provides practitioners with an overview of the principles and methods needed to build reliable biometric systems. It covers three main topics: key biometric technologies, design and management issues, and the performance evaluation of biometric systems for personal verification/identification. The four most widely used technologies are focused on - speech, fingerprint, iris and face recognition. Key features include: in-depth coverage of the technical and practical obstacles which are often neglected by application developers and system integrators and which result in shortfalls between expected and actual performance; and protocols and benchmarks which will allow developers to compare performance and track system improvements.

A credible voter register gives legitimacy to the electoral process and helps prevent electoral fraud. However, voter registration remains a complex and contested task. It is one of the most important activities that an electoral management body needs to conduct, but it is also one of the most costly in terms of both time and resources. Many countries that face challenges in creating an accurate voter register are considering reforming their voter registration systems through the introduction of biometric technologies. The drive towards biometrics has been facilitated by its largely apolitical nature. Investing in high-tech solutions allows stakeholders to demonstrate their commitment to resolving electoral problems. At the same time, expectations on biometric solutions may be exaggerated. This guide provides an overview of key concepts and considerations for all stakeholders involved in discussions about the application of biometrics in elections, both for voter registration before an election and for voter verification at polling stations on election day.