Erschienen: 12.02.2016 Abbildung von Mason / Traoré / Woungang | Machine Learning Techniques for Gait Biometric Recognition | 1st ed. 2016 | 2016 | Using the Ground Reaction Forc...

Mason / Traoré / Woungang

Machine Learning Techniques for Gait Biometric Recognition

Using the Ground Reaction Force

1st ed. 2016 2016. Buch. xxxiv, 223 S. 73 s/w-Abbildungen, 3 Farbabbildungen, 37 s/w-Tabelle, 2 Farbtabellen, Bibliographien. Hardcover

Springer. ISBN 978-3-319-29086-7

Format (B x L): 15,5 x 23,5 cm

Gewicht: 555 g

In englischer Sprache


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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