Machine learning techniques for gait biometric recognition [electronic resource] : using the ground reaction force / James Eric Mason, Issa Traoré, Isaac Woungang.
Mason, James Eric, author.; Traoré, Issa, author.; Woungang, Isaac, author.
2016
Q325.5
Available Online

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Title Machine learning techniques for gait biometric recognition [electronic resource] : using the ground reaction force / James Eric Mason, Issa Traoré, Isaac Woungang.
Author Mason, James Eric, author.
ISBN 9783319290881 (electronic book)
3319290886 (electronic book)
9783319290867
DOI https://doi.org/10.1007/978-3-319-29088-1
Published Switzerland : Springer, [2016]
Copyright ©2016
Language English
Description 1 online resource.
Item Number 10.1007/978-3-319-29088-1 doi
Call Number Q325.5
Dewey Decimal Classification 006.31
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (viewed February 10, 2016).
Added Author Traoré, Issa, author.
Woungang, Isaac, author.
Available in Other Form Machine Learning Techniques for Gait Biometric Recognition : Using the Ground Reaction Force
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Introduction
Background
Experimental Design and Dataset
Feature Extraction.-Normalization
Classification
Measured Performance
Experimental Analysis
Conclusion.

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