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.
introduces novel machine-learning-based temporal normalization
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