Machine Learning Techniques for Gait Biometric Recognition

Using the Ground Reaction Force

Автор: JAMES ERIC MASON

Език:

Корици: С меки корици

Издател: Springer International Publishing AG

Наличност: Външен склад

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This book focuses on how machine learning techniques can be used to analyze and make use of one part...

Информация за книгата

Автор

JAMES ERIC MASON

Език

Английски език

Корици

Книга - С меки корици

Издадена

2018

страници

223

EAN

9783319804866

ISBN

9783319804866

Enbook ID

19308760

Издател

Springer International Publishing AG

Теглоt

414

Размери

233 x 156 x 20

Пълно описание

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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Machine Learning Techniques for Gait Biometric Recognition

Using the Ground Reaction Force

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