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Face Recognition Using Independent Component Analysis
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Book Synopsis Independent Component Analysis of Edge Information for Face Recognition by : Kailash Jagannath Karande
Download or read book Independent Component Analysis of Edge Information for Face Recognition written by Kailash Jagannath Karande and published by Springer. This book was released on 2013-07-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents research work on face recognition using edge information as features for face recognition with ICA algorithms. The independent components are extracted from edge information. These independent components are used with classifiers to match the facial images for recognition purpose. In their study, authors have explored Canny and LOG edge detectors as standard edge detection methods. Oriented Laplacian of Gaussian (OLOG) method is explored to extract the edge information with different orientations of Laplacian pyramid. Multiscale wavelet model for edge detection is also proposed to extract edge information. The book provides insights for advance research work in the area of image processing and biometrics.
Author :Marian Stewart Bartlett Publisher :Springer Science & Business Media ISBN 13 :1461516374 Total Pages :181 pages Book Rating :4.4/5 (615 download)
Book Synopsis Face Image Analysis by Unsupervised Learning by : Marian Stewart Bartlett
Download or read book Face Image Analysis by Unsupervised Learning written by Marian Stewart Bartlett and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
Book Synopsis Face Recognition Using Independent Component Analysis by : Kailash Karande
Download or read book Face Recognition Using Independent Component Analysis written by Kailash Karande and published by LAP Lambert Academic Publishing. This book was released on 2012-08 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Independent Component Analysis (ICA) plays very important role in blind source separation and has many more applications in pattern recognition. The ICA is new area for researchers in the last decade for face recognition. There is much more scope for research using ICA for face recognition with different methods of feature extractions and needs to be addressed. As the promising applications of ICA is feature extraction, where it extracts independent image bases which are not necessarily orthogonal and it is sensitive to high order statistics. In the task of face recognition, important information may be contained in the high order relationship among pixels. Independent Component Analysis (ICA) minimizes both second order and higher-order dependencies in the input data and attempts to find the basis along with the data when projected onto them are statistically independent. So ICA seems to be a promising face feature extraction method.
Author :Marian Stewart Bartlett Publisher :Springer Science & Business Media ISBN 13 :9780792373483 Total Pages :194 pages Book Rating :4.3/5 (734 download)
Book Synopsis Face Image Analysis by Unsupervised Learning by : Marian Stewart Bartlett
Download or read book Face Image Analysis by Unsupervised Learning written by Marian Stewart Bartlett and published by Springer Science & Business Media. This book was released on 2001-06-30 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
Book Synopsis Face Detection and Recognition by : Asit Kumar Datta
Download or read book Face Detection and Recognition written by Asit Kumar Datta and published by Chapman and Hall/CRC. This book was released on 2015-08-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art, offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then: Explores subspace methods for dimensionality reduction in face image processing, statistical methods applied to face detection, and intelligent face detection methods dominated by the use of artificial neural networks Covers face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory, face recognition using evolutionary algorithms, and face recognition in frequency domain Discusses methods for the localization of face landmarks helpful in face recognition, methods of generating synthetic face images using set estimation theory, and databases of face images available for testing and training systems Features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB®/PYTHON) and hardware implementation strategies with code examples Demonstrates how frequency domain correlation techniques can be used supplying exhaustive test results Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition.
Book Synopsis Recent Advances in Face Recognition by : Kresimir Delac
Download or read book Recent Advances in Face Recognition written by Kresimir Delac and published by BoD – Books on Demand. This book was released on 2008-12-01 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main idea and the driver of further research in the area of face recognition are security applications and human-computer interaction. Face recognition represents an intuitive and non-intrusive method of recognizing people and this is why it became one of three identification methods used in e-passports and a biometric of choice for many other security applications. This goal of this book is to provide the reader with the most up to date research performed in automatic face recognition. The chapters presented use innovative approaches to deal with a wide variety of unsolved issues.
Author :Kailash Jagannath Karande Publisher :Springer Science & Business Media ISBN 13 :8132215125 Total Pages :85 pages Book Rating :4.1/5 (322 download)
Book Synopsis Independent Component Analysis of Edge Information for Face Recognition by : Kailash Jagannath Karande
Download or read book Independent Component Analysis of Edge Information for Face Recognition written by Kailash Jagannath Karande and published by Springer Science & Business Media. This book was released on 2013-07-15 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents research work on face recognition using edge information as features for face recognition with ICA algorithms. The independent components are extracted from edge information. These independent components are used with classifiers to match the facial images for recognition purpose. In their study, authors have explored Canny and LOG edge detectors as standard edge detection methods. Oriented Laplacian of Gaussian (OLOG) method is explored to extract the edge information with different orientations of Laplacian pyramid. Multiscale wavelet model for edge detection is also proposed to extract edge information. The book provides insights for advance research work in the area of image processing and biometrics.
Book Synopsis Face Recognition Using Neural Networks and Principal Component Analysis by : Carlos L. Castillo
Download or read book Face Recognition Using Neural Networks and Principal Component Analysis written by Carlos L. Castillo and published by . This book was released on 2003 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reviews, Refinements and New Ideas in Face Recognition by : Peter Corcoran
Download or read book Reviews, Refinements and New Ideas in Face Recognition written by Peter Corcoran and published by BoD – Books on Demand. This book was released on 2011-07-27 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a baby one of our earliest stimuli is that of human faces. We rapidly learn to identify, characterize and eventually distinguish those who are near and dear to us. We accept face recognition later as an everyday ability. We realize the complexity of the underlying problem only when we attempt to duplicate this skill in a computer vision system. This book is arranged around a number of clustered themes covering different aspects of face recognition. The first section on Statistical Face Models and Classifiers presents reviews and refinements of some well-known statistical models. The next section presents two articles exploring the use of Infrared imaging techniques and is followed by few articles devoted to refinements of classical methods. New approaches to improve the robustness of face analysis techniques are followed by two articles dealing with real-time challenges in video sequences. A final article explores human perceptual issues of face recognition.
Book Synopsis Handbook of Face Recognition by : Stan Z. Li
Download or read book Handbook of Face Recognition written by Stan Z. Li and published by Springer. This book was released on 2011-11-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers. This handbook is a comprehensive account of face recognition research and technology, written by a group of leading international researchers. Twelve chapters cover all the sub-areas and major components for designing operational face recognition systems. Background, modern techniques, recent results, and challenges and future directions are considered. The book is aimed at practitioners and professionals planning to work in face recognition or wanting to become familiar with the state-of- the-art technology. A comprehensive handbook, by leading research authorities, on the concepts, methods, and algorithms for automated face detection and recognition. Essential reference resource for researchers and professionals in biometric security, computer vision, and video image analysis.
Book Synopsis Independent Component Analysis by : Te-Won Lee
Download or read book Independent Component Analysis written by Te-Won Lee and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, blind source separation by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues. This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem). The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification. Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.
Book Synopsis Face Image Analysis by Unsupervised Learning by : Marian Stewart Bartlett
Download or read book Face Image Analysis by Unsupervised Learning written by Marian Stewart Bartlett and published by Springer. This book was released on with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
Book Synopsis Face Recognition & Principal Component Analysis Method by : Liton Chandra Paul
Download or read book Face Recognition & Principal Component Analysis Method written by Liton Chandra Paul and published by . This book was released on 2013-09-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Face Recognition written by Miloš Oravec and published by BoD – Books on Demand. This book was released on 2010-04-01 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Book Synopsis Face Recognition by : Harry Wechsler
Download or read book Face Recognition written by Harry Wechsler and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: The NATO Advanced Study Institute (ASI) on Face Recognition: From Theory to Applications took place in Stirling, Scotland, UK, from June 23 through July 4, 1997. The meeting brought together 95 participants (including 18 invited lecturers) from 22 countries. The lecturers are leading researchers from academia, govemment, and industry from allover the world. The lecturers presented an encompassing view of face recognition, and identified trends for future developments and the means for implementing robust face recognition systems. The scientific programme consisted of invited lectures, three panels, and (oral and poster) presentations from students attending the AS!. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (i) human processing of face recognition and its relevance to forensic systems, (ii) face coding, (iii) connectionist methods and support vector machines (SVM), (iv) hybrid methods for face recognition, and (v) predictive learning and performance evaluation. The goals of the panels were to provide links among the lectures and to emphasis the themes of the meeting. The topics of the panels were: (i) How the human visual system processes faces, (ii) Issues in applying face recognition: data bases, evaluation and systems, and (iii) Classification issues involved in face recognition. The presentations made by students gave them an opportunity to receive feedback from the invited lecturers and suggestions for future work.
Book Synopsis Face Image Analysis by Unsupervised Learning and Redundancy Reduction by : Marian Stewart Bartlett
Download or read book Face Image Analysis by Unsupervised Learning and Redundancy Reduction written by Marian Stewart Bartlett and published by . This book was released on 1998 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Eigenface written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-05-14 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Eigenface An eigenface is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification. The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Eigenface Chapter 2: Principal component analysis Chapter 3: Singular value decomposition Chapter 4: Eigenvalues and eigenvectors Chapter 5: Eigendecomposition of a matrix Chapter 6: Kernel principal component analysis Chapter 7: Matrix analysis Chapter 8: Linear dynamical system Chapter 9: Multivariate normal distribution Chapter 10: Modes of variation (II) Answering the public top questions about eigenface. (III) Real world examples for the usage of eigenface in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Eigenface.
