Discriminative Learning in Biometrics
This monograph describes the latest advances in discriminative learning methods for biometric recognition. Specifically, it focuses on three representative categories of methods: sparse representation-based classification, metric learning, and discriminat
- PDF / 7,742,419 Bytes
- 267 Pages / 453.543 x 683.15 pts Page_size
- 56 Downloads / 232 Views
criminative Learning in Biometrics
Discriminative Learning in Biometrics
David Zhang Yong Xu Wangmeng Zuo •
Discriminative Learning in Biometrics
123
David Zhang Department of Computing The Hong Kong Polytechnic University Kowloon, Hong Kong China
Wangmeng Zuo Harbin Institute of Technology Harbin, Heilongjiang China
Yong Xu Shenzhen Graduate School Harbin Institute of Technology Shenzhen, Guangdong China
ISBN 978-981-10-2055-1 DOI 10.1007/978-981-10-2056-8
ISBN 978-981-10-2056-8
(eBook)
Library of Congress Control Number: 2016953313 © Springer Science+Business Media Singapore 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #22-06/08 Gateway East, Singapore 189721, Singapore
Preface
Biometrics, defined as the automated recognition of individuals based on their behavioral and physiological characteristics, can provide more stable and feasible solutions to personal verification and identification than traditional token-based and knowledge-based methods. In the past few decades, tremendous progress has been achieved in biometrics. Versatile biometric recognition techniques have been developed, including fingerprint, face, iris, palmprint, and ear recognition, and various biometric systems have been deployed in the applications of assess control, forensics, border crossing, network security, etc. Recently, with the ever-growing need for reliable authentication of human identity in the complex physical and network environments, much attention have been given to biometric research for reliable recognition of low-quality data or under unconstrained scenarios, which has imposed new challenges to the field of biometrics. The development and popularization of sensors shed some light on improving the accuracy and stability of biometric recognition. The deployment of national ID progra
Data Loading...
