Verwandte Artikel zu Machine Learning Techniques for Gait Biometric Recognition:...

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

 
9783319804866: Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force

Inhaltsangabe

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

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

James Eric Mason 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 Examining the impact of Normalization and Footwear on Gait Biometrics Recognition using the Ground Reaction Force, 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.

Issa Traore 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.

Isaac Woungang 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.

Von der hinteren Coverseite

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

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

EUR 13,81 für den Versand von Vereinigtes Königreich nach USA

Versandziele, Kosten & Dauer

Weitere beliebte Ausgaben desselben Titels

9783319290867: Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force

Vorgestellte Ausgabe

ISBN 10:  331929086X ISBN 13:  9783319290867
Verlag: Springer, 2016
Hardcover

Suchergebnisse für Machine Learning Techniques for Gait Biometric Recognition:...

Beispielbild für diese ISBN

Mason, James Eric; Traoré, Issa; Woungang, Isaac
Verlag: Springer, 2018
ISBN 10: 3319804863 ISBN 13: 9783319804866
Neu Softcover

Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: New. In. Artikel-Nr. ria9783319804866_new

Verkäufer kontaktieren

Neu kaufen

EUR 60,32
Währung umrechnen
Versand: EUR 13,81
Von Vereinigtes Königreich nach USA
Versandziele, Kosten & Dauer

Anzahl: Mehr als 20 verfügbar

In den Warenkorb

Foto des Verkäufers

James Eric Mason
ISBN 10: 3319804863 ISBN 13: 9783319804866
Neu Taschenbuch

Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 260 pp. Englisch. Artikel-Nr. 9783319804866

Verkäufer kontaktieren

Neu kaufen

EUR 53,49
Währung umrechnen
Versand: EUR 60,00
Von Deutschland nach USA
Versandziele, Kosten & Dauer

Anzahl: 2 verfügbar

In den Warenkorb

Foto des Verkäufers

James Eric Mason
ISBN 10: 3319804863 ISBN 13: 9783319804866
Neu Taschenbuch

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9783319804866

Verkäufer kontaktieren

Neu kaufen

EUR 53,49
Währung umrechnen
Versand: EUR 62,00
Von Deutschland nach USA
Versandziele, Kosten & Dauer

Anzahl: 1 verfügbar

In den Warenkorb