Machine learning techniques for gait biometric recognition [electronic resource] : using the ground reaction force / James Eric Mason, Issa Traoré, Isaac Woungang.
2016
Q325.5
Linked e-resources
Linked Resource
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Machine learning techniques for gait biometric recognition [electronic resource] : using the ground reaction force / James Eric Mason, Issa Traoré, Isaac Woungang.
Author
ISBN
9783319290881 (electronic book)
3319290886 (electronic book)
9783319290867
3319290886 (electronic book)
9783319290867
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
Available in Other Form
Linked Resources
Record Appears in
Table of Contents
Introduction
Background
Experimental Design and Dataset
Feature Extraction.-Normalization
Classification
Measured Performance
Experimental Analysis
Conclusion.
Background
Experimental Design and Dataset
Feature Extraction.-Normalization
Classification
Measured Performance
Experimental Analysis
Conclusion.