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Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study.

online signature verification --- shape contexts --- function features --- SC-DTW --- symbolic representation --- two-stage method --- finger features --- multimodal recognition --- local coding --- Gabor filter --- LGS --- human identification --- biomarker --- ECG --- machine learning --- Physionet --- Lviv Biometric Dataset --- biometry --- identification --- bloodstream --- image recognition --- multi-biometrics --- bit planes --- block --- mutual information --- cross-device --- dorsal hand vein recognition --- person re-identification --- superpixel --- temporally aligned pooling --- walking cycle --- automatic recognition --- face --- voice --- body motion --- autism spectrum disorder (ASD) --- assessment --- intervention --- curve similarity --- curve similarity model --- curve similarity transformation --- similarity distance --- segmentation matching --- evolutionary computation --- finger vein recognition --- hand vein recognition --- contactless acquisition device --- public vascular pattern dataset --- biometric recognition performance evaluation --- face verification --- optical correlation --- Hausdorff distance --- image classification --- face detection --- depth map ensemble --- filtering --- geometric deep learning --- ear detection --- structured prediction --- semantic segmentation --- rotation equivariance --- Gaussian mixture model --- superpixels --- face recognition systems --- person identification --- biometric systems --- survey --- automatic signature verification --- touch-screen sensor --- data quality --- enrollment phase --- performance assessment --- augmented signature --- security enhancement --- mobile conditions --- biometric recognition --- visible light iris images --- image quality assessment --- image covariates --- quality filtering --- vascular biometric recognition --- wrist vein recognition --- contactless dataset --- pattern recognition --- infrared camera --- non-contact devices --- Scale-Invariant Feature Transform (SIFT®) --- Speeded Up Robust Features (SURF®) --- Oriented FAST and Rotated BRIEF (ORB) --- fingerprint --- presentation attack detection --- deep learning

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This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects. .

Computational intelligence. --- Artificial intelligence. --- Biomedical engineering. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Health informatics. --- Computational Intelligence. --- Artificial Intelligence. --- Health Informatics. --- Biomedical Engineering and Bioengineering. --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Data processing