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During the last 30 years, face recognition and related problems such as face detection/tracking and facial expression recognition have attracted researchers from both the engineering and psychology communities. In addition, extensive research has been carried out to study hand and body gestures. The understanding of how humans perceive these important cues has significant scientific value and extensive applications. For example, human-computer interaction, visual surveillance, and smart video indexing are active application areas. Aiming towards putting such amazing perception capability onto computer systems, researchers have made substantial progress. However, technological challenges still exist in many aspects. Following a format similar to the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG) 2003, this one-day workshop (AMFG 2005) provided a focused international forum to bring together well-known researchers and research groups to review the status of recognition, analysis and modeling of faces and gestures, to discuss the challenges that we are facing, and to explore future directions. Overall, 30 papers were selected from 90 submitted manuscripts. The topics of these papers range from feature representation, robust recognition, learning, and 3D modeling to psychology. In addition, two invited talks were given, by Prof. Kanade and Dr. Phillips. The technical program was organized into four oral sessions and two poster sessions. This workshop would not have been possible without the timely reviews provided by the members of the Technical Program Committee under a tight schedule. October 2005 Wenyi Zhao Shaogang Gong Xiaoou Tang
This book constitutes the refereed proceedings of the Third International Workshop on Analysis and Modelling of Faces and Gestures, AMFG 2007, held within the scope of ICCV 2007, the International Conference on Computer Vision. The papers review the status of recognition, analysis and modeling of face, gesture, activity, and behavior. Topics addressed include feature representation, 3D face, video-based face recognition, facial motion analysis, and sign recognition.
Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control.Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face de
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
'Face Processing' seeks to answer questions such as how we recognise familiar faces, and which factors determine facial attractiveness. Drawing on a wealth of studies and research, it is an essential companion for undergraduates studying face processing as part of a psychology degree.
Intelligent recognition methods have recently proven to be indispensable in a variety of modern industries, including computer vision, robotics, medical imaging, visualization and the media. Furthermore, they play a critical role in the traditional fields such as character recognition, natural language processing and personal identification. This cutting-edge book draws together the latest findings of industry experts and researchers from around the globe. It is a timely guide for all those require comprehensive, state-of-the-art advice on the present status and future potential of intelligent recognition technology. Computer-Aided Intelligent Recognition Techniques and Applications: Provides the user community with systems and tools for application in a very wide range of areas, including: IT, education, security, banking, police, postal services, manufacturing, mining, medicine, multimedia, entertainment, communications, data visualization, knowledge extraction, pattern classification and virtual reality. Disseminates information in a plethora of disciplines, for example pattern recognition, AI, image processing, computer vision and graphics, neural networks, cryptography, fuzzy logic, databases, evolutionary algorithms, shape and numerical analysis. Illustrates all theory with real-world examples and case studies. This valuable resource is essential reading for computer scientists, engineers, and consultants requiring up-to-date comprehensive guidance on the latest developments in computer-aided intelligent recognition techniques and applications. Its detailed, practical approach will be of interest to senior undergraduate and graduate students as well as researchers and industry experts in the field of intelligent recognition.
This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
Ann Macintosh Napier University, UK The papers in this volume are the refereed application papers presented at ES2001, the Twenty-fIrst SGES International Conference on Knowledge Based Systems and Applied ArtifIcial Intelligence, held in Cambridge in December 200 I. The scope of the application papers has expanded over recent years to cover not just innovative applications using traditional knowledge based systems, but also to include applications demonstrating the whole range of AI technologies. These papers continue to illustrate the maturity of AI as a commercially viable technology to solve real world problems. The papers were subject to refereeing by at least two expert referees. All papers that were in any way controversial were discussed in depth by the Application Programme Committee. For the ES2001 Application Stream, a paper is acceptable even if it describes a system that has not yet been installed, provided the application is original and the paper discusses the kind of things that would help others needing to solve a similar problem. Papers have been selected to highlight critical areas of success - and failure - and to present the benefIts and lessons learnt to other developers. This volume contains sixteen papers describing deployed or emerging applications in a range of diverse areas: business and commerce, engineering, manufacturing, knowledge and information management, and music.
Based on a rigorous selection from 58 proposals coming from across the world, this volume will include some of the most recent ideas and technical results in computer systems, computer science, and computer-communication networks. The book will offer the reader with a timely access to innovative research from many different areas of the world where advances in computing and communications are created.
This book resulted from a series of frustrations. Analytical electron microscopy requires exactly what its name implies: quantitative information to conduct an analysis. The frustrations arose when I started hunting for specific forms of equations in a form understandable to a non-crystaHographer, for definitions of subtle concepts related to crystallography, for intelligible interpretations of space group symbols and their significance. What I frequently discovered was that such information was buried in a giant tome and couched in terms familiar to crystallographers but not to electron microscopists in general, or it was located in an old reference not available in my library, or it was found in an out-of-print book, or it was in a Russian book no longer available, etc. So to minimize the frustrations, I started a notebook containing the details, particularly after I had found forms of equations useful for quick calculations or equations in a form useful for proving, doing, or extending calculations found in a reference. The resulting notebook grew to a respectable size, requiring some organizing of the contents. Finally, the size became large enough, and has proven useful enough, to produce the notebook as a book.