9783319290867 - Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force di Mason, James Eric; Traore, Issa; Woungang, Isaac (15 risultati)

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Condizione: New. pp. 223.

Hardcover. Condizione: Brand New. 260 pages. 9.25x6.25x0.75 inches. In Stock.

Buch. Condizione: 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.

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 260 | Sprache: Englisch | Produktart: Bücher | 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.

Condizione: new. Questo è un articolo print on demand.

Buch. Condizione: 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.

Condizione: New. Print on Demand pp. 223.

Condizione: New. PRINT ON DEMAND pp. 223.

Condizione: 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.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.