Details
Machine Learning Techniques for Gait Biometric Recognition
Using the Ground Reaction Force|
53,49 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 04.02.2016 |
| ISBN/EAN: | 9783319290881 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
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.<div><br></div><div><p>This book</p><p>· introduces novel machine-learning-based temporal normalization techniques</p><p>· bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition</p>· provides detailed discussions of key research challenges and open research issues in gait biometrics recognition</p><p>· compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear</p></div>
Introduction.- Background.- Experimental Design and Dataset.- Feature Extraction.-Normalization.- Classification.- Measured Performance.- Experimental Analysis.- Conclusion.
<p><b>James Eric Mason</b>
obtained his BSEng and MASc from the University of Victoria, Canada, in 2009
and 2014, respectively. During his Master’s program, under the supervision of
Dr. Issa Traore, his research focused primarily on biometric security solutions
with a particular emphasis on the gait biometric. In 2014 he completed his
thesis titled <i>Examining the impact of
Normalization and Footwear on Gait Biometrics Recognition using the Ground
Reaction Force</i>, which served as an inspiration for the work presented in
this book. His research interests include biometric security, machine learning,
software engineering, web development, and weather/climate sciences. Since
2011, he has been working with the software startup Referral SaaSquatch as a
full stack software developer.</p>
<p><b>Issa Traore</b>
obtained a PhD in Software Engineering in 1998 from Institute Nationale
Polytechnique (INPT)-LAAS/CNRS, Toulouse, France. He has been with the faculty
of the Department of Electrical and Computer Engineering of the University of
Victoria since 1999. He is currently a Full Professor and the Coordinator of
the Information Security and object Technology (ISOT) Lab at the University of Victoria. His research interests include biometrics
technologies, computer intrusion detection, network forensics, software
security, and software quality engineering.
He is currently serving as Associate Editor for the International
Journal of Communication Systems (IJCS) and the International Journal of
Communication Networks and Distributed Systems (IJCNDS). Dr. Traore is also a
co-founder and Chief Scientist of Plurilock Security Solutions Inc., a network security company which provides
innovative authentication technologies, and is one of the pioneers in bringing
behavioral biometric authentication products to the market.</p><p></p>
<p><b>Isaac Woungang </b>received
his M.Sc. & Ph.D degrees, all in Mathematics, from the University of Aix
Marseille II, France, and University of South, Toulon and Var, France, in 1990
and 1994 respectively. In 1999, he received a MSc degree from the
INRS-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 been
with Ryerson University, where he is now a full professor of Computer Science
and Director of the Distributed Applications and Broadband (DABNEL) Lab. His current research interests include
radio resource management in next generation wireless networks, biometrics
technologies, network security. Dr. Woungang has published 8 books and over 89
refereed technical articles in scholarly international journals and proceedings
of international conferences. He has served as Associate Editor of the
Computers and Electrical Engineering (Elsevier), and the International Journal
of Communication Systems (Wiley). He has Guest Edited several Special Issues
withvarious reputed journals such as IET Information Security, Mathematical
and Computer Modeling (Elsevier), Computer Communications (Elsevier), Computers
and Electrical Engineering (Elsevier), and Telecommunication Systems
(Springer). Since January 2012, He serves as Chair of Computer Chapter, IEEE
Toronto Section.<br></p><p></p>
obtained his BSEng and MASc from the University of Victoria, Canada, in 2009
and 2014, respectively. During his Master’s program, under the supervision of
Dr. Issa Traore, his research focused primarily on biometric security solutions
with a particular emphasis on the gait biometric. In 2014 he completed his
thesis titled <i>Examining the impact of
Normalization and Footwear on Gait Biometrics Recognition using the Ground
Reaction Force</i>, which served as an inspiration for the work presented in
this book. His research interests include biometric security, machine learning,
software engineering, web development, and weather/climate sciences. Since
2011, he has been working with the software startup Referral SaaSquatch as a
full stack software developer.</p>
<p><b>Issa Traore</b>
obtained a PhD in Software Engineering in 1998 from Institute Nationale
Polytechnique (INPT)-LAAS/CNRS, Toulouse, France. He has been with the faculty
of the Department of Electrical and Computer Engineering of the University of
Victoria since 1999. He is currently a Full Professor and the Coordinator of
the Information Security and object Technology (ISOT) Lab at the University of Victoria. His research interests include biometrics
technologies, computer intrusion detection, network forensics, software
security, and software quality engineering.
He is currently serving as Associate Editor for the International
Journal of Communication Systems (IJCS) and the International Journal of
Communication Networks and Distributed Systems (IJCNDS). Dr. Traore is also a
co-founder and Chief Scientist of Plurilock Security Solutions Inc., a network security company which provides
innovative authentication technologies, and is one of the pioneers in bringing
behavioral biometric authentication products to the market.</p><p></p>
<p><b>Isaac Woungang </b>received
his M.Sc. & Ph.D degrees, all in Mathematics, from the University of Aix
Marseille II, France, and University of South, Toulon and Var, France, in 1990
and 1994 respectively. In 1999, he received a MSc degree from the
INRS-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 been
with Ryerson University, where he is now a full professor of Computer Science
and Director of the Distributed Applications and Broadband (DABNEL) Lab. His current research interests include
radio resource management in next generation wireless networks, biometrics
technologies, network security. Dr. Woungang has published 8 books and over 89
refereed technical articles in scholarly international journals and proceedings
of international conferences. He has served as Associate Editor of the
Computers and Electrical Engineering (Elsevier), and the International Journal
of Communication Systems (Wiley). He has Guest Edited several Special Issues
withvarious reputed journals such as IET Information Security, Mathematical
and Computer Modeling (Elsevier), Computer Communications (Elsevier), Computers
and Electrical Engineering (Elsevier), and Telecommunication Systems
(Springer). Since January 2012, He serves as Chair of Computer Chapter, IEEE
Toronto Section.<br></p><p></p>
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.<div><p>This book </p>
<p>·
introduces novel machine-learning-based temporal normalization
techniques</p>
<p>·
bridges research gaps concerning the effect of footwear and
stepping speed on footstep GRF-based person recognition</p>
<p>·
provides detailed discussions of key research challenges and open
research issues in gait biometrics recognition</p>
<p>·
compares biometrics systems trained and tested with the same
footwear against those trained and tested with different footwear</p></div>
<p>·
introduces novel machine-learning-based temporal normalization
techniques</p>
<p>·
bridges research gaps concerning the effect of footwear and
stepping speed on footstep GRF-based person recognition</p>
<p>·
provides detailed discussions of key research challenges and open
research issues in gait biometrics recognition</p>
<p>·
compares biometrics systems trained and tested with the same
footwear against those trained and tested with different footwear</p></div>
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
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