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Machine Learning Techniques for Gait Biometric Recognition

Using the Ground Reaction Force

  • James Eric Mason
  • Issa Traoré
  • Isaac Woungang

Table of contents

  1. Front Matter
    Pages i-xxxiv
  2. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 1-7
  3. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 9-35
  4. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 37-51
  5. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 53-87
  6. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 89-110
  7. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 111-156
  8. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 157-173
  9. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 175-188
  10. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 189-202
  11. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 203-208
  12. James Eric Mason, Issa Traoré, Isaac Woungang
    Pages 209-215
  13. Back Matter
    Pages 217-223

About this book

Introduction

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

Keywords

Behavioral biometrics Biometrics Recognition framework Footstep GRF-based person recognition GRF Recognition Ground Reaction Force (GRF)-based Gait Machine learning

Authors and affiliations

  • James Eric Mason
    • 1
  • Issa Traoré
    • 2
  • Isaac Woungang
    • 3
  1. 1.University of VictoriaVICTORIACanada
  2. 2.University of VictoriaVICTORIACanada
  3. 3.Ryerson UniversityTorontoCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-29088-1
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-29086-7
  • Online ISBN 978-3-319-29088-1
  • Buy this book on publisher's site
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