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Machine Learning Techniques for Gait Biometric Recognition

Language EnglishEnglish
Book Hardback
Book Machine Learning Techniques for Gait Biometric Recognition James Eric Mason
Libristo code: 02876432
Publishers Springer International Publishing AG, February 2016
This book focuses on how machine learning techniques can be used to analyze and make use of one part... Full description
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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.

About the book

Full name Machine Learning Techniques for Gait Biometric Recognition
Author James Eric Mason, Issa Traore, Isaac Woungang
Language English
Binding Book - Hardback
Date of issue 2016
Number of pages 223
EAN 9783319290867
ISBN 331929086X
Libristo code 02876432
Publishers Springer International Publishing AG
Weight 5148
Dimensions 155 x 235 x 20

Categories

Technology, Engineering, Agriculture > Civil engineering, surveying & building > Building construction & materials > Security & fire alarm systems
Technology, Engineering, Agriculture > Other technologies & applied sciences > Applied optics > Imaging systems & technology
Computing & Information Technology > Computer science > Artificial intelligence > Machine learning
Computing & Information Technology > Computer science > Artificial intelligence > Pattern recognition
Computing & Information Technology > Computer science > Artificial intelligence > Computer vision
Technology, Engineering, Agriculture > Technology: general issues > Engineering: general
Technology, Engineering, Agriculture > Electronics & communications engineering > Electronics engineering
Computing & Information Technology > Computer science > Signal processing
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