Machine Learning Techniques for Gait Biometric Recognition Using the
This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framewo
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ne Learning Techniques for Gait Biometric Recognition Using the Ground Reaction Force
Machine Learning Techniques for Gait Biometric Recognition
James Eric Mason Issa Traoré Isaac Woungang •
Machine Learning Techniques for Gait Biometric Recognition Using the Ground Reaction Force
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Isaac Woungang Ryerson University Toronto, ON Canada
James Eric Mason University of Victoria Victoria, BC Canada Issa Traoré University of Victoria Victoria, BC Canada
ISBN 978-3-319-29086-7 DOI 10.1007/978-3-319-29088-1
ISBN 978-3-319-29088-1
(eBook)
Library of Congress Control Number: 2015960234 © Springer International Publishing Switzerland 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 SpringerNature The registered company is Springer International Publishing AG Switzerland
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Preface
The last two decades have seen a dramatic increase in the number of stakeholders of biometric technologies. The quality of the technologies has increased due to an improvement in underlying data processing and sensor technologies. A growing and healthy marketplace has emerged, while the number of people using, operating, or impacted by these technologies has been growing exponentially. Several new disruptive technologies have emerged, along with the diversification of the devices and platforms where biometrics are provisioned. The ubiquity of mobile phones and the multiplicity and diversity of sensors available for biometric provisioning (e.g., webcam, fingerprint reader, touchscreen, accelerometer, gyroscope, etc.) is contributing significantl
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