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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force - Softcover
- VerlagSpringer
- Erscheinungsdatum2018
- ISBN 10 3319804863
- ISBN 13 9783319804866
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten260
- Kontakt zum HerstellerNicht verfügbar
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Machine Learning Techniques for Gait Biometric Recognition
Verlag:
Springer International Publishing, 2018
ISBN 10: 3319804863
ISBN 13: 9783319804866
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Softcover
Anbieter: moluna, Greven, Deutschland
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces novel machine-learning-based temporal normalization techniquesBridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognitionProvides detailed discussions of key research challe. Bestandsnummer des Verkäufers 448755578
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Machine Learning Techniques for Gait Biometric Recognition : Using the Ground Reaction Force
ISBN 10: 3319804863
ISBN 13: 9783319804866
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783319804866
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Machine Learning Techniques for Gait Biometric Recognition
Verlag:
Springer International Publishing Mrz 2018, 2018
ISBN 10: 3319804863
ISBN 13: 9783319804866
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 260 pp. Englisch. Bestandsnummer des Verkäufers 9783319804866
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Machine Learning Techniques for Gait Biometric Recognition
ISBN 10: 3319804863
ISBN 13: 9783319804866
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 260 pp. Englisch. Bestandsnummer des Verkäufers 9783319804866
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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force
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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force
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Zustand: New. PRINT ON DEMAND pp. 223. Bestandsnummer des Verkäufers 18379854513
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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force
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Machine Learning Techniques for Gait Biometric Recognition : Using the Ground Reaction Force
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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force
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Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force
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