Machine Learning Techniques for Gait Biometric Recognition

Using the Ground Reaction Force

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Science & Nature, Technology, Electronics
Cover of the book Machine Learning Techniques for Gait Biometric Recognition by Isaac Woungang, Issa Traoré, James Eric Mason, Springer International Publishing
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Author: Isaac Woungang, Issa Traoré, James Eric Mason ISBN: 9783319290881
Publisher: Springer International Publishing Publication: February 4, 2016
Imprint: Springer Language: English
Author: Isaac Woungang, Issa Traoré, James Eric Mason
ISBN: 9783319290881
Publisher: Springer International Publishing
Publication: February 4, 2016
Imprint: Springer
Language: English

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

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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