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Machine Learning Techniques for Gait Biometric Recognition = Using the Ground Reaction Force /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning Techniques for Gait Biometric Recognition/ by James Eric Mason, Issa Traoré, Isaac Woungang.
Reminder of title:	Using the Ground Reaction Force /
Author:	Mason, James Eric.
other author:	Traoré, Issa.
Description:	XXXIV, 223 p. 76 illus., 3 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Signal processing. -
Online resource:	https://doi.org/10.1007/978-3-319-29088-1
ISBN:	9783319290881

Machine Learning Techniques for Gait Biometric Recognition = Using the Ground Reaction Force /
Mason, James Eric.

Machine Learning Techniques for Gait Biometric RecognitionUsing the Ground Reaction Force /[electronic resource] :by James Eric Mason, Issa Traoré, Isaac Woungang. - 1st ed. 2016. - XXXIV, 223 p. 76 illus., 3 illus. in color.online resource.

Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.

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.

ISBN: 9783319290881

Standard No.: 10.1007/978-3-319-29088-1doiSubjects--Topical Terms:

561459
Signal processing.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Machine Learning Techniques for Gait Biometric Recognition = Using the Ground Reaction Force /
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