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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
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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