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This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.

Biometric identification. --- Deepfakes. --- Image processing --- Morphing (Computer animation). --- Digital techniques. --- Biometric person authentication --- Biometrics (Identification) --- Anthropometry --- Identification --- Computer-generated metamorphosis --- Metamorphosing (Computer animation) --- Computer animation --- Digital image processing --- Digital electronics --- Deep fakes --- Deepfake AI --- Disinformation --- Forgery --- DeepFakes --- Face Manipulation Detection --- Media Forensic --- Biometric Recognition --- Image Processing and Pattern Recognition --- Open Access --- Identificació biomètrica --- Processament digital d'imatges --- Desinformació --- Morphing (Computer animation)

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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 Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.

History of engineering & technology --- multi-objective evolutionary algorithms --- rule-based classifiers --- interpretable machine learning --- categorical data --- hand sign language --- deep learning --- restricted Boltzmann machine (RBM) --- multi-modal --- profoundly deaf --- noisy image --- ensemble methods --- adaptive classifiers --- recurrent concepts --- concept drift --- stock price direction prediction --- toe-off detection --- gait event --- silhouettes difference --- convolutional neural network --- saliency detection --- foggy image --- spatial domain --- frequency domain --- object contour detection --- discrete stationary wavelet transform --- attention allocation --- attention behavior --- hybrid entropy --- information entropy --- single pixel single photon image acquisition --- time-of-flight --- action recognition --- fibromyalgia --- Learning Using Concave and Convex Kernels --- Empatica E4 --- self-reported survey --- speech emotion recognition --- 3D convolutional neural networks --- k-means clustering --- spectrograms --- context-aware framework --- accuracy --- false negative rate --- individual behavior estimation --- statistical-based time-frequency domain and crowd condition --- emotion recognition --- gestures --- body movements --- Kinect sensor --- neural networks --- face analysis --- face segmentation --- head pose estimation --- age classification --- gender classification --- singular point detection --- boundary segmentation --- blurring detection --- fingerprint image enhancement --- fingerprint quality --- speech --- committee of classifiers --- biometric recognition --- multimodal-based human identification --- privacy --- privacy-aware

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This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.

History of engineering & technology --- multi-objective evolutionary algorithms --- rule-based classifiers --- interpretable machine learning --- categorical data --- hand sign language --- deep learning --- restricted Boltzmann machine (RBM) --- multi-modal --- profoundly deaf --- noisy image --- ensemble methods --- adaptive classifiers --- recurrent concepts --- concept drift --- stock price direction prediction --- toe-off detection --- gait event --- silhouettes difference --- convolutional neural network --- saliency detection --- foggy image --- spatial domain --- frequency domain --- object contour detection --- discrete stationary wavelet transform --- attention allocation --- attention behavior --- hybrid entropy --- information entropy --- single pixel single photon image acquisition --- time-of-flight --- action recognition --- fibromyalgia --- Learning Using Concave and Convex Kernels --- Empatica E4 --- self-reported survey --- speech emotion recognition --- 3D convolutional neural networks --- k-means clustering --- spectrograms --- context-aware framework --- accuracy --- false negative rate --- individual behavior estimation --- statistical-based time-frequency domain and crowd condition --- emotion recognition --- gestures --- body movements --- Kinect sensor --- neural networks --- face analysis --- face segmentation --- head pose estimation --- age classification --- gender classification --- singular point detection --- boundary segmentation --- blurring detection --- fingerprint image enhancement --- fingerprint quality --- speech --- committee of classifiers --- biometric recognition --- multimodal-based human identification --- privacy --- privacy-aware