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Machine Learning Techniques for Gait Biometric Recognition Using the Ground Reaction Force /
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....
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 008 | 160204s2016 sz | s |||| 0|eng d | ||
| 020 | |a 9783319290881 |9 978-3-319-29088-1 | ||
| 024 | 7 | |a 10.1007/978-3-319-29088-1 |2 doi | |
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| 100 | 1 | |a Mason, James Eric. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |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. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a XXXIV, 223 p. 76 illus., 3 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion. | |
| 520 | |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. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Biometric identification. | |
| 650 | 0 | |a Security systems. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Biometrics. |
| 650 | 2 | 4 | |a Security Science and Technology. |
| 700 | 1 | |a Traoré, Issa. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Woungang, Isaac. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319290867 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319290874 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319804866 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-29088-1 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||