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
