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This book, authored by Ajay Kumar, delves into the advanced methodologies of iris and periocular recognition using deep learning techniques. It discusses various innovations in biometric systems, focusing on areas such as iris segmentation and recognition, network architectures for optimized iris templates, and feature encoding. The book examines the effectiveness of convolutional neural networks in enhancing recognition accuracy and explores dynamic iris recognition frameworks. It is intended for researchers and practitioners in the field of biometrics and computer vision, offering insights into the latest trends and challenges in iris recognition technologies.

Biometric identification. --- Iris (Eye) --- Biometric identification

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This book provides readers with the basic understanding regarding the threats to the voice processing systems, the state-of-the-art defense methods as well as the current research results on securing voice processing systems. It also introduces three mechanisms to secure the voice processing systems against malicious voice attacks under different scenarios, by utilizing time-domain signal waves, frequency-domain spectrum features and acoustic physical attributes. First, the authors uncover the modulated replay attack, which uses an inverse filter to compensate for the spectrum distortion caused by the replay attacks to bypass the existing spectrum-based defenses. The authors also provide an effective defense method that utilizes both the time-domain artifacts and frequency-domain distortion to detect the modulated replay attacks. Second, the book introduces a secure automatic speech recognition system for driverless car to defeat adversarial voice commandattacks launched from car loudspeakers, smartphones and passengers. Third, it provides an acoustic compensation system design to reduce the effects from the spectrum reduction attacks, by the audio spectrum compensation and acoustic propagation principle. Finally, the authors conclude with their research effort on defeating the malicious voice attacks and provide insights into more secure voice processing systems. This book is intended for security researchers, computer scientists and electrical engineers who are interested in the research areas of biometrics, speech signal processing, IoT security and audio security. Advanced-level students who are studying these topics will benefit from this book as well.

Data protection --- Biometric identification. --- Privacy. --- Biometrics. --- Law and legislation.

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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 todevices 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.

Biometric identification. --- Pattern recognition systems. --- Data protection --- Biometrics. --- Automated Pattern Recognition. --- Privacy. --- Law and legislation.

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This handbook covers the essential subject areas of cutaneous biometrics, including acne, aging, genetics, eczema, psoriasis, atopic dermatitis, rosacea, wound healing, and more. Erythema of rosacea and the risk of skin ulceration associated with oral nicorandil therapy are covered in detail. Various areas of acne are addressed, including acne severity grading, the assessment of general health and quality of life in patients with acne, and the effect of automated online counseling on clinical outcomes and quality of life among adolescents with acne vulgaris. Other topics include the development and validation of a clinical scale for the evaluation of forearm skin photoaging and a scoring system for mucosal disease severity. This is an ideal reference for biomedical engineering researchers and clinicians working in the field of dermatology interested in a deeper understanding of cutaneous biometrics and how it can be applied to their work. This book also: Broadens reader's understanding of the importance of creating meaningful dermatological patient outcome measurements and the use of the physician global assessment in a clinical setting to measure and track patient outcomes Details the guidelines on the measurement of ultraviolet radiation levels in ultraviolet phototherapy .

Biometric identification. --- Biotechnology. --- Biomedical engineering. --- Dermatology. --- Biometrics. --- Biomedical Engineering and Bioengineering.

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Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight. The book starts from introducing methods for model-driven feature extraction and classification, including basic computer vision techniques for extracting high-level semantics from images. A brief overview of probabilistic classification with generative and discriminative classifiers is then provided. Next, neural networks are presented as a means to learn a classification model directly from labeled sample images, with individual components of the network discussed. The relationships between network components and those of a traditional designed model are explored, and different concepts for regularizing model training are explained. Finally, various methods for analyzing what a network has learned are covered in the closing section of the book. The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.

Computer vision. --- Machine learning. --- Pattern recognition systems. --- Biometric identification. --- Artificial intelligence --- Computer Vision. --- Machine Learning. --- Automated Pattern Recognition. --- Biometrics. --- Data Science. --- Data processing.