The human face is perhaps the most familiar and easily recognized object in the world, yet both its three-dimensional shape and its two-dimensional images are complex and hard to characterize. This book develops the vocabulary of ridges and parabolic curves, of illumination eigenfaces and elastic warpings for describing the perceptually salient fea
This book deals with a case: the elucidation of the intricate and detailed patterns of the human face using the tools of 'pattern theory', of statistical pattern recognition and of differential geometry. It is an outcome of the work at the Harvard Robotics Laboratory in the early 1990s.
This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006. The papers cover ontology-based retrieval and annotation, ranking and similarity measurements, music information retrieval, visual modeling, adaptive retrieval, structuring multimedia, as well as user integration and profiling.
"This 10-volume compilation of authoritative, research-based articles contributed by thousands of researchers and experts from all over the world emphasized modern issues and the presentation of potential opportunities, prospective solutions, and future directions in the field of information science and technology"--Provided by publisher.
This book aims to bring together selected recent advances, applications and original results in the area of biometric face recognition. They can be useful for researchers, engineers, graduate and postgraduate students, experts in this area and hopefully also for people interested generally in computer science, security, machine learning and artificial intelligence. Various methods, approaches and algorithms for recognition of human faces are used by authors of the chapters of this book, e.g. PCA, LDA, artificial neural networks, wavelets, curvelets, kernel methods, Gabor filters, active appearance models, 2D and 3D representations, optical correlation, hidden Markov models and others. Also a broad range of problems is covered: feature extraction and dimensionality reduction (chapters 1-4), 2D face recognition from the point of view of full system proposal (chapters 5-10), illumination and pose problems (chapters 11-13), eye movement (chapter 14), 3D face recognition (chapters 15-19) and hardware issues (chapters 19-20).
Face recognition has received substantial attention from researchers in biometrics, computer vision, pattern recognition, and cognitive psychology communities because of the increased attention being devoted to security, man-machine communication, content-based image retrieval, and image/video coding. We have proposed two automated recognition paradigms to advance face recognition technology. Three major tasks involved in face recognition systems are: (i) face detection, (ii) face modeling, and (iii) face matching. We have developed a face detection algorithm for color images in the presence of various lighting conditions as well as complex backgrounds. Our detection method first corrects the color bias by a lighting compensation technique that automatically estimates the parameters of reference white for color correction. We overcame the difficulty of detecting the low-luma and high-luma skin tones by applying a nonlinear transformation to the Y CbCr color space. Our method generates face candidates based on the spatial arrangement of detected skin patches. We constructed eye, mouth, and face boundary maps to verify each face candidate. Experimental results demonstrate successful detection of faces with different sizes, color, position, scale, orientation, 3D pose, and expression in several photo collections. 3D human face models augment the appearance-based face recognition approaches to assist face recognition under the illumination and head pose variations. For the two proposed recognition paradigms, we have designed two methods for modeling human faces based on (i) a generic 3D face model and an individual's facial measurements of shape and texture captured in the frontal view, and (ii) alignment of a semantic face graph, derived from a generic 3D face model, onto a frontal face image.
This volume contains the latest in the series of ICAPR proceedings on the state-of-the-art of different facets of pattern recognition. These conferences have already carved out a unique position among events attended by the pattern recognition community. The contributions tackle open problems in the classic fields of image and video processing, document analysis and multimedia object retrieval as well as more advanced topics in biometrics speech and signal analysis. Many of the papers focus both on theory and application driven basic research pattern recognition.
The purpose of this book, entitled Face Analysis, Modeling and Recognition Systems is to provide a concise and comprehensive coverage of artificial face recognition domain across four major areas of interest: biometrics, robotics, image databases and cognitive models. Our book aims to provide the reader with current state-of-the-art in these domains. The book is composed of 12 chapters which are grouped in four sections. The chapters in this book describe numerous novel face analysis techniques and approach many unsolved issues. The authors who contributed to this book work as professors and researchers at important institutions across the globe, and are recognized experts in the scientific fields approached here. The topics in this book cover a wide range of issues related to face analysis and here are offered many solutions to open issues. We anticipate that this book will be of special interest to researchers and academics interested in computer vision, biometrics, image processing, pattern recognition and medical diagnosis.
This handbook is an essential, comprehensive resource for students and academics interested in topics in cognitive psychology, including perceptual issues, attention, memory, knowledge representation, language, emotional influences, judgment, problem solving, and the study of individual differences in cognition.
NEW YORK TIMES BESTSELLER • The bold futurist and renowned author of The Singularity Is Near explores the limitless potential of reverse-engineering the human brain. “This book is a Rosetta Stone for the mystery of human thought.”—Martine Rothblatt, chairman and CEO, United Therapeutics, and creator of Sirius XM Satellite Radio “Kurzweil’s vision of our super-enhanced future is completely sane and calmly reasoned, and his book should nicely smooth the path for the earth’s robot overlords, who, it turns out, will be us.”—The New York Times In How to Create a Mind, Ray Kurzweil presents a provocative exploration of the most important project in human-machine civilization: reverse-engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines. Kurzweil discusses how the brain functions, how the mind emerges, brain-computer interfaces, and the implications of vastly increasing the powers of our intelligence to address the world’s problems. He also thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating. Drawing on years of advanced research and cutting-edge inventions in artificial intelligence, How to Create a Mind is an incredible synthesis of neuroscience and technology and provides a road map for the future of human progress.
