Książki

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

Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force

Introduction.- Background.- Experimental Design and Dataset.- Feature Extraction.-Normalization.- Classification.- Measured Performance.- Experimental Analysis.- Conclusion.