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Machine Learning Techniques for Gait Biometric Recognition [electronic resource] : Using the Ground Reaction Force / by James Eric Mason, Issa Traor�e, Isaac Woungang.

By: Mason, James Eric [author.].
Contributor(s): Traor�e, Issa [author.] | Woungang, Isaac [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: XXXIV, 223 p. 76 illus., 73 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319290881.Subject(s): Engineering | Biometrics (Biology) | System safety | Engineering | Signal, Image and Speech Processing | Biometrics | Security Science and TechnologyAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.
In: Springer eBooksSummary: 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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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.

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