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03301nam a22005295i 4500 |
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|a 9783319290881
|9 978-3-319-29088-1
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|a 10.1007/978-3-319-29088-1
|2 doi
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|d GrThAP
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|a TK5102.9
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|a 621.382
|2 23
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|a Mason, James Eric.
|e author.
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|a Machine Learning Techniques for Gait Biometric Recognition
|h [electronic resource] :
|b Using the Ground Reaction Force /
|c by James Eric Mason, Issa Traoré, Isaac Woungang.
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| 250 |
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|a 1st ed. 2016.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
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| 300 |
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|a XXXIV, 223 p. 76 illus., 73 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.
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|a 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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|a Engineering.
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|a Biometrics (Biology).
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|a System safety.
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|a Engineering.
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|a Signal, Image and Speech Processing.
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| 650 |
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|a Biometrics.
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| 650 |
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|a Security Science and Technology.
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| 700 |
1 |
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|a Traoré, Issa.
|e author.
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| 700 |
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|a Woungang, Isaac.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783319290867
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| 856 |
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|u http://dx.doi.org/10.1007/978-3-319-29088-1
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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