Handbook of Face Recognition
Stan Z. Li Anil K. Jain
Editors
Handbook of
Face Recognition
With 210 Illustrations
Stan Z. Li Anil K. Jain
Center for Biometrics Research and Testing & Department of Computer Science
National Lab of Pattern Recognition & Engineering
Institute of Automation Michigan State University
Chinese Academy of Sciences East Lansing, MI 48824-1226
Beijing 100080 USA
China jain@cse.msu.edu
szli@nlpr.ia.ac.cn
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 I. Li, S. Z., 1958– II. Jain,
Anil K., 1948–
TA1650.H36 2004
006.4′2—dc22 2004052453
ISBN 0-387-40595-X Printed on acid-free paper.
© 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)
987654321 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-
