Machine Learning Techniques for Gait Biometric Recognition [electronic resource] : Using the Ground Reaction Force / by James Eric Mason, Issa Traoré, Isaac Woungang.
By: Mason, James Eric [author.]
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Contributor(s): Traoré, Issa [author.]
| Woungang, Isaac [author.]
| SpringerLink (Online service)
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Material type: ![materialTypeLabel](/opac-tmpl/lib/famfamfam/BK.png)
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Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.
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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