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Author: Bob Zhang
Publisher: World Scientific
ISBN: 9813234598
Category : Computers
Languages : en
Pages : 428
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Book Description
What features or information can we observe from a face, and how can these information help us to understand the person concerned, in terms of their well-being and what can we learn about and from each given feature? This book answers these questions by first dividing a face's multiple characteristics into two main categories: original (or physiological) features and features that change over a lifetime. The first category, original features, may be further divided into two sub-classes: features special (or unique) to an individual, and features common to a particular group. The second, changed features, can also be subdivided into two groups: features altered due to disease or features altered by other external factors. From these four sub-categories, four different applications — facial identification using original and special features; beauty analysis using original common features; facial diagnosis by disease changed features; and expression recognition through affect-changed features — are identified.The book will benefit researchers, professionals, and graduate students working in the field of computer vision, pattern recognition, security/clinical practice, and beauty analysis, and will also be useful for interdisciplinary research.
Author: Bob Zhang
Publisher: World Scientific
ISBN: 9813234598
Category : Computers
Languages : en
Pages : 428
Get Book Here
Book Description
What features or information can we observe from a face, and how can these information help us to understand the person concerned, in terms of their well-being and what can we learn about and from each given feature? This book answers these questions by first dividing a face's multiple characteristics into two main categories: original (or physiological) features and features that change over a lifetime. The first category, original features, may be further divided into two sub-classes: features special (or unique) to an individual, and features common to a particular group. The second, changed features, can also be subdivided into two groups: features altered due to disease or features altered by other external factors. From these four sub-categories, four different applications — facial identification using original and special features; beauty analysis using original common features; facial diagnosis by disease changed features; and expression recognition through affect-changed features — are identified.The book will benefit researchers, professionals, and graduate students working in the field of computer vision, pattern recognition, security/clinical practice, and beauty analysis, and will also be useful for interdisciplinary research.
Author: 张一博
Publisher:
ISBN: 9787040494471
Category : Human face recognition (Computer science)
Languages : en
Pages : 395
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Book Description
Author: Fadi Dornaika
Publisher: Bentham Science Publishers
ISBN: 1681081105
Category : Computers
Languages : en
Pages : 264
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Book Description
Advances in Face Image Analysis: Theory and applications describes several approaches to facial image analysis and recognition. Eleven chapters cover advances in computer vision and pattern recognition methods used to analyze facial data. The topics addressed in this book include automatic face detection, 3D face model fitting, robust face recognition, facial expression recognition, face image data embedding, model-less 3D face pose estimation and image-based age estimation. The chapters are also written by experts from a different research groups. Readers will, therefore, have access to contemporary knowledge on facial recognition with some diverse perspectives offered for individual techniques. The book is a useful resource for a wide audience such as i) researchers and professionals working in the field of face image analysis, ii) the entire pattern recognition community interested in processing and extracting features from raw face images, and iii) technical experts as well as postgraduate computer science students interested in cutting edge concepts of facial image recognition.
Author: Mohamed Daoudi
Publisher: John Wiley & Sons
ISBN: 1118592638
Category : Technology & Engineering
Languages : en
Pages : 219
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Book Description
3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further. The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied. Key features: • Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition • Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images • Contains numerous exercises and algorithms throughout the book
Author: Yi Li
Publisher: Springer Nature
ISBN: 9811391483
Category : Computers
Languages : en
Pages : 104
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Book Description
This book presents a comprehensive review of heterogeneous face analysis and synthesis, ranging from the theoretical and technical foundations to various hot and emerging applications, such as cosmetic transfer, cross-spectral hallucination and face rotation. Deep generative models have been at the forefront of research on artificial intelligence in recent years and have enhanced many heterogeneous face analysis tasks. Not only has there been a constantly growing flow of related research papers, but there have also been substantial advances in real-world applications. Bringing these together, this book describes both the fundamentals and applications of heterogeneous face analysis and synthesis. Moreover, it discusses the strengths and weaknesses of related methods and outlines future trends. Offering a rich blend of theory and practice, the book represents a valuable resource for students, researchers and practitioners who need to construct face analysis systems with deep generative networks.
Author: Shubham Mahajan
Publisher: John Wiley & Sons
ISBN: 1394230931
Category : Computers
Languages : en
Pages : 372
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Book Description
A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy. Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.
Author: Vasant Manohar
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
ABSTRACT: Deformable modeling of facial soft tissues have found use in application domains such as human-machine interaction for facial expression recognition. More recently, such modeling techniques have been used for tasks like age estimation and person identification. This dissertation is focused on development of novel image analysis algorithms to follow facial strain patterns observed through video recording of faces in expressions. Specifically, we use the strain pattern extracted from non-rigid facial motion as a simplified and adequate way to characterize the underlying material properties of facial soft tissues. Such an approach has several unique features. Strain pattern instead of the image intensity is used as a classification feature. Strain is related to biomechanical properties of facial tissues that are distinct for each individual. Strain pattern is less sensitive to illumination differences (between enrolled and query sequences) and face camouflage because the strain pattern of a face remains stable as long as reliable facial deformations are captured. A finite element modeling based method enforces regularization which mitigates issues (such as temporal matching and noise sensitivity) related to automatic motion estimation. Therefore, the computational strategy is accurate and robust. Images or videos of facial deformations are acquired with video camera and without special imaging equipment. Experiments using range images on a dataset consisting of 50 subjects provide the necessary proof of concept that strain maps indeed have a discriminative value. On a video dataset containing 60 subjects undergoing a particular facial expression, experimental results using the computational strategy presented in this work emphasize the discriminatory and stability properties of strain maps across adverse data conditions (shadow lighting and face camouflage). Such properties make it a promising feature for image analysis tasks that can benefit from such auxiliary information about the human face. Strain maps add a new dimension in our abilities to characterize a human face. It also fosters newer ways to capture facial dynamics from video which, if exploited efficiently, can lead to an improved performance in tasks involving the human face. In a subsequent effort, we model the material constants (Young's modulus) of the skin in sub-regions of the face from the motion observed in multiple facial expressions. On a public database consisting of 40 subjects undergoing some set of facial motions, we present an expression invariant strategy to matching faces using the Young's modulus of the skin. Such an efficient way of describing underlying material properties from the displacements observed in video has an important application in deformable modeling of physical objects which are usually gauged by their simplicity and adequacy. The contributions through this work will have an impact on the broader vision community because of its highly novel approaches to the long-standing problem of motion analysis of elastic objects. In addition, the value is the cross disciplinary nature and its focus on applying image analysis algorithms to the rather difficult and important problem of material property characterization of facial soft tissues and their applications. We believe this research provides a special opportunity for the utilization of video processing to enhance our abilities to make unique discoveries through the facial dynamics inherent in video.
Author: Ioan Opris
Publisher: Frontiers Media SA
ISBN: 2889635422
Category :
Languages : en
Pages : 243
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Book Description
Author: Yuguang Liu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"Unconstrained face detection is the task of robustly finding and locating faces in an image subject to possible variations in facial scale, blur, pose, illumination, occlusion, and facial expression. It is a critical first step towards a host of modern surveillance applications, including but not limited to face verification, face recognition, face tracking, and human-computer interaction. Though much progress has been made in unconstrained face detection during the past decade, the majority of work focuses on improving the detection robustness on variations caused by blur, pose, illumination, occlusion and facial expression. Facial scale, despite its immense influence on face detection accuracy, has received much less attention than have the above factors. This is partially due to the fact that most traditional face detection benchmark datasets tend to collect faces of relatively large size and with modest scale variation. Nonetheless, in real-world applications, such as surveillance systems, it is imperative to possess an equal ability to detect both big faces (close to camera) and tiny ones (far away from the camera) at the same time. To the best of our knowledge, no published face detection algorithm can detect a face as large as 1000 x 1000 pixels while simultaneously detecting another one as small as 10 x 10 pixels within a single image with similarly high accuracy.We introduce a Multi-Path Face Detection Network (MP-FDN) to filter an image for simultaneously proposing and verifying different sized faces in parallel paths. This is the first time that faces across a large span of scales are detected by a single network with forked detection paths. More importantly, the division of the paths are not handcrafted, but totally based on the scale sensitivity inherent in the convolutional networks that was also discovered in this thesis for the first time. MP-FDN consists of two stages. The first stage is a Multi-Path Face Proposal Network (MP-FPN) that suggests faces at three different scale ranges. This design is based on our observation that the hierarchical multi-scale layers of deep convolutional networks (ConvNet) can inherently represent face patterns at multiple scales. In particular, low-level ConvNet layers are more sensitive to tiny faces, while high-level ConvNet layers are more discriminative to big faces. To this end, MP-FPN utilizes three parallel outputs of the convolutional feature maps to simultaneously predict small, medium and large candidate face regions, respectively. The second stage is a Multi-Path Face Verification Network (MP-FVN) that further eliminates false positives while including false negatives. MP-FVN utilizes the same three parallel paths as MP-FPN. For each detection path, it pools features from both a face candidate region (provided by MP-FPN) and a larger contextual region (surrounding the face candidate region). These facial and contextual features are then concatenated to provide a more accurate "faceness" probability to the face candidate. Note that the network structure and hyper-parameters of MP-FPN and MP-FVN are completely based on controlled experiments, rather than being "handcrafted". To testify to the performance of MP-FDN on the basis its ability to perform face detection, we conducted comprehensive experiments on two challenging public face detection benchmark datasets: WIDER FACE and FDDB datasets. MP-FDN consistently achieves better than the state-of-the-art performance on both of them. Specifically, on the most challenging so-called "hard partition" of WIDER FACE test set that contains faces as small as about 9 pixels and as large as more than 1000 pixels in height, MP-FDN outperforms the former best result by 9.8% for the Average Precision. This demonstrates that MP-FDN is a viable and accurate face detector for unconstrained face detection, especially in the case of large scale variations." --
Author: Adamo Quaglia
Publisher: Nova Science Publishers
ISBN: 9781619426634
Category : Human face recognition (Computer science)
Languages : en
Pages : 0
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Book Description
Face recognition has been an active area of research in image processing and computer vision due to its extensive range of prospective applications relating to biometrics, information security, video surveillance, law enforcement, identity authentication, smart cards, and access control systems. Topics discussed in this compilation include two-dimensional principal component analysis algorithms for face recognition; principal component analysis (PCA) and artificial immune networks in face recognition; multi-class learning facial age estimation and forensic face recognition.
