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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force de James Eric Mason

Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force

Autor James Eric Mason, Issa Traoré, Isaac Woungang
en Limba Engleză Hardback – 12 feb 2016
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 framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF.

This book
·         introduces novel machine-learning-based temporal normalization techniques
·         bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition
·         provides detailed discussions of key research challenges and open research issues in gait biometrics recognition ·         compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear
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Specificații

ISBN-13: 9783319290867
ISBN-10: 331929086X
Pagini: 223
Ilustrații: XXXIV, 223 p. 76 illus., 3 illus. in color.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.54 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Introduction.- Background.- Experimental Design and Dataset.- Feature Extraction.-Normalization.- Classification.- Measured Performance.- Experimental Analysis.- Conclusion.

Notă biografică

James Eric Masonobtained his BSEng and MASc from the University of Victoria, Canada, in 2009and 2014, respectively. During his Master’s program, under the supervision ofDr. Issa Traore, his research focused primarily on biometric security solutionswith a particular emphasis on the gait biometric. In 2014 he completed histhesis titled Examining the impact ofNormalization and Footwear on Gait Biometrics Recognition using the GroundReaction Force, which served as an inspiration for the work presented inthis book. His research interests include biometric security, machine learning,software engineering, web development, and weather/climate sciences. Since2011, he has been working with the software startup Referral SaaSquatch as afull stack software developer.
Issa Traoreobtained a PhD in Software Engineering in 1998 from Institute NationalePolytechnique (INPT)-LAAS/CNRS, Toulouse, France. He has been with the facultyof the Department of Electrical and Computer Engineering of the University ofVictoria since 1999. He is currently a Full Professor and the Coordinator ofthe Information Security and object Technology (ISOT) Lab at the University of Victoria. His research interests include biometricstechnologies, computer intrusion detection, network forensics, softwaresecurity, and software quality engineering. He is currently serving as Associate Editor for the InternationalJournal of Communication Systems (IJCS) and the International Journal ofCommunication Networks and Distributed Systems (IJCNDS). Dr. Traore is also aco-founder and Chief Scientist of Plurilock Security Solutions Inc., a network security company which providesinnovative authentication technologies, and is one of the pioneers in bringingbehavioral biometric authentication products to the market.
Isaac Woungang receivedhis M.Sc. & Ph.D degrees, all in Mathematics, from the University of AixMarseille II, France, and University of South, Toulon and Var, France, in 1990and 1994 respectively. In 1999, he received a MSc degree from theINRS-Materials and Telecommunications, University of Quebec, Montreal, QC,Canada. From 1999 to 2002, he worked as a software engineer at Nortel Networks,Ottawa, Canada, in the Photonic Line Systems Group. Since 2002, he has beenwith Ryerson University, where he is now a full professor of Computer Scienceand Director of the Distributed Applications and Broadband (DABNEL) Lab. His current research interests includeradio resource management in next generation wireless networks, biometricstechnologies, network security. Dr. Woungang has published 8 books and over 89refereed technical articles in scholarly international journals and proceedingsof international conferences. He has served as Associate Editor of theComputers and Electrical Engineering (Elsevier), and the International Journalof Communication Systems (Wiley). He has Guest Edited several Special Issueswithvarious reputed journals such as IET Information Security, Mathematicaland Computer Modeling (Elsevier), Computer Communications (Elsevier), Computersand Electrical Engineering (Elsevier), and Telecommunication Systems(Springer). Since January 2012, He serves as Chair of Computer Chapter, IEEEToronto Section.

Textul de pe ultima copertă

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 framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF.
This book
·        introduces novel machine-learning-based temporal normalizationtechniques
·        bridges research gaps concerning the effect of footwear andstepping speed on footstep GRF-based person recognition
·        provides detailed discussions of key research challenges and openresearch issues in gait biometrics recognition
·        compares biometrics systems trained and tested with the samefootwear against those trained and tested with different footwear

Caracteristici

Introduces novel machine-learning-based temporal normalization techniques Bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition Provides detailed discussions of key research challenges and open research issues in gait biometrics recognition Compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear Includes supplementary material: sn.pub/extras