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Handbook of Face Recognition

Stan Z. Li Anil K. Jain Editors Handbook of Face Recognition With 210 Illustrations

Stan Z. Li Center for Biometrics Research and Testing & Anil K. Jain Department of Computer Science National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China szli@nlpr.ia.ac.cn & Engineering Michigan State University East Lansing, MI 48824-1226 USA jain@cse.msu.edu Library of Congress Cataloging-in-Publication Data Handbook of face recognition / editors, Stan Z. Li & Anil K. Jain. p. cm. Includes bibliographical references and index. ISBN 0-387-40595-X (alk. paper) 1. Human face recognition (Computer science Anil K., 1948– TA1650.H36 006.4′2—dc22 2004 I. Li, S. Z., 1958– II. Jain, 2004052453 Printed on acid-free paper. ISBN 0-387-40595-X © 2005 Springer Science+Business Media, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, Inc., 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in con- nection with reviews or scholarly analysis. Use in connection with any form of infor- mation storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America. (MP) 9 8 7 6 5 4 3 2 1 SPIN 10946602 springeronline.com

Preface Face recognition has a large number of applications, including security, person veriﬁcation, In- ternet communication, and computer entertainment. Although research in automatic face recog- nition has been conducted since the 1960s, this problem is still largely unsolved. Recent years have seen signiﬁcant progress in this area owing to advances in face modeling and analysis techniques. Systems have been developed for face detection and tracking, but reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including ris- ing public concern for security, the need for identity veriﬁcation in the digital world, and the need for face analysis and modeling techniques in multimedia data management and computer entertainment. Recent advances in automated face analysis, pattern recognition, and machine learning have made it possible to develop automatic face recognition systems to address these applications. This book was written based on two primary motivations. The ﬁrst was the need for highly reliable, accurate face recognition algorithms and systems. The second was the recent research in image and object representation and matching that is of interest to face recognition re- searchers. The book is intended for practitioners and students who plan to work in face recognition or who want to become familiar with the state-of-the-art in face recognition. It also provides ref- erences for scientists and engineers working in image processing, computer vision, biometrics and security, Internet communications, computer graphics, animation, and the computer game industry. The material ﬁts the following categories: advanced tutorial, state-of-the-art survey, and guide to current technology. The book consists of 16 chapters, covering all the subareas and major components nec- essary for designing operational face recognition systems. Each chapter focuses on a speciﬁc topic or system component, introduces background information, reviews up-to-date techniques, presents results, and points out challenges and future directions. Chapter 1 introduces face recognition processing, including major components such as face detection, tracking, alignment, and feature extraction, and it points out the technical challenges of building a face recognition system. We emphasize the importance of subspace analysis and learning, not only providing an understanding of the challenges therein but also the most suc-

VI Preface cessful solutions available so far. In fact, most technical chapters represent subspace learning- based techniques for various steps in face recognition. Chapter 2 reviews face detection techniques and describes effective statistical learning methods. In particular, AdaBoost-based learning methods are described because they often achieve practical and robust solutions. Techniques for dealing with nonfrontal face detection are discussed. Results are presented to compare boosting algorithms and other factors that af- fect face detection performance. Chapters 3 and 4 discuss face modeling methods for face alignment. These chapters de- scribe methods for localizing facial components (e.g., eyes, nose, mouth) and facial outlines and for aligning facial shape and texture with the input image. Input face images may be ex- tracted from static images or video sequences, and parameters can be extracted from these input images to describe the shape and texture of a face. These results are based largely on advances in the use of active shape models and active appearance models. Chapters 5 and 6 cover topics related to illumination and color. Chapter 5 describes recent advances in illumination modeling for faces. The illumination invariant facial feature repre- sentation is described; this representation improves the recognition performance under varying illumination and inspires further explorations of reliable face recognition solutions. Chapter 6 deals with facial skin color modeling, which is helpful when color is used for face detection and tracking. Chapter 7 provides a tutorial on subspace modeling and learning-based dimension reduction methods, which are fundamental to many current face recognition techniques. Whereas the col- lection of all images constitutes high dimensional space, images of faces reside in a subspace of that space. Facial images of an individual are in a subspace of that subspace. It is of paramount importance to discover such subspaces so as to extract effective features and construct robust classiﬁers. Chapter 8 addresses problems of face tracking and recognition from a video sequence of images. The purpose is to make use of temporal constraints present in the sequence to make tracking and recognition more reliable. Chapters 9 and 10 present methods for pose and illumination normalization and extract effective facial features under such changes. Chapter 9 describes a model for extracting illu- mination invariants, which were previously presented in Chapter 5. Chapter 9 also presents a subregion method for dealing with variation in pose. Chapter 10 describes a recent innovation, called Morphable Models, for generative modeling and learning of face images under changes in illumination and pose in an analysis-by-synthesis framework. This approach results in algo- rithms that, in a sense, generalize the alignment algorithms described in Chapters 3 and 4 to the situation where the faces are subject to large changes in illumination and pose. In this work, the three-dimensional data of faces are used during the learning phase to train the model in addition to the normal intensity or texture images. Chapters 11 and 12 provide methods for facial expression analysis and synthesis. The analysis part, Chapter 11, automatically analyzes and recognizes facial motions and facial fea- ture changes from visual information. The synthesis part, Chapter 12, describes techniques on three-dimensional face modeling and animation, face lighting from a single image, and facial expression synthesis. These techniques can potentially be used for face recognition with vary- ing poses, illuminations, and facial expressions. They can also be used for human computer interfaces.

Preface VII Chapter 13 reviews 27 publicly available databases for face recognition, face detection, and facial expression analysis. These databases provide a common ground for development and evaluation of algorithms for faces under variations in identity, face pose, illumination, facial expression, age, occlusion, and facial hair. Chapter 14 introduces concepts and methods for face veriﬁcation and identiﬁcation perfor- mance evaluation. The chapter focuses on measures and protocols used in FERET and FRVT (face recognition vendor tests). Analysis of these tests identiﬁes advances offered by state-of- the-art technologies for face recognition, as well as the limitations of these technologies. Chapter 15 offers psychological and neural perspectives suggesting how face recognition might go on in the human brain. Combined ﬁndings suggest an image-based representation that encodes faces relative to a global average and evaluates deviations from the average as an indication of the unique properties of individual faces. Chapter 16 describes various face recognition applications, including face identiﬁcation, security, multimedia management, and human-computer interaction. The chapter also reviews many face recognition systems and discusses related issues in applications and business. Acknowledgments A number of people helped in making this book a reality. Vincent Hsu, Dirk Colbry, Xiaoguang Lu, Karthik Nandakumar, and Anoop Namboodiri of Michigan State University, and Shiguang Shan, Zhenan Sun, Chenghua Xu and Jiangwei Li of the Chinese Academy of Sciences helped proofread several of the chapters. We also thank Wayne Wheeler and Ann Kostant, editors at Springer, for their suggestions and for keeping us on schedule for the production of the book. This handbook project was done partly when Stan Li was with Microsoft Research Asia. December 2004 Stan Z. Li Beijing, China Anil K. Jain East Lansing, Michigan

Contents Chapter 1. Introduction Stan Z. Li, Anil K. Jain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Chapter 2. Face Detection Stan Z. Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Chapter 3. Modeling Facial Shape and Appearance Tim Cootes, Chris Taylor, Haizhuang Kang, Vladimir Petrovi´c . . . . . . . . . . . . . . . . . . . . . . 39 Chapter 4. Parametric Face Modeling and Tracking J¨orgen Ahlberg, Fadi Dornaika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Chapter 5. Illumination Modeling for Face Recognition Ronen Basri, David Jacobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Chapter 6. Facial Skin Color Modeling J. Birgitta Martinkauppi, Matti Pietik¨ainen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Color Plates for Chapters 6 and 15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Chapter 7. Face Recognition in Subspaces Gregory Shakhnarovich, Baback Moghaddam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Chapter 8. Face Tracking and Recognition from Video Rama Chellappa, Shaohua Kevin Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Chapter 9. Face Recognition Across Pose and Illumination Ralph Gross, Simon Baker, Iain Matthews, Takeo Kanade . . . . . . . . . . . . . . . . . . . . . . . . . 193 Chapter 10. Morphable Models of Faces Sami Romdhani, Volker Blanz, Curzio Basso, Thomas Vetter . . . . . . . . . . . . . . . . . . . . . . 217

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