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
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introduces novel machine-learning-based temporal normalization
techniques
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bridges research gaps concerning the effect of footwear and
stepping speed on footstep GRF-based person recognition
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provides detailed discussions of key research challenges and open
research issues in gait biometrics recognition
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compares biometrics systems trained and tested with the same
footwear against those trained and tested with different footwear
