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

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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.

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

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Detail Information

Series Title

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Call Number

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Publisher

Cham : Springer Cham., 2016

Collation

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Language

English

ISBN/ISSN

978-3-319-29088-1

Classification

NONE

Content Type

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Media Type

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Edition

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Specific Detail Info

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Statement of Responsibility

James Eric Mason, Issa Traoré, Isaac Woungang

Other Information

Cataloger

Kholif Basri

Source

https://link.springer.com/book/10.1007/978-3-319-29088-1

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