Machine Learning Techniques for Gait Biometric Recognition

Using the Ground Reaction Force

• Author(s): James Eric Mason, Issa Traoré, Isaac Woungang,
• Publisher: Springer
• Pages: 223
• ISBN_10: 3319290886
  ISBN_13: 9783319290881
  
• Language: en
• Categories: Technology & Engineering / Electronics / General , Computers / Artificial Intelligence / Computer Vision & Pattern Recognition , Technology & Engineering / Engineering (General) , Technology & Engineering / Imaging Systems , Technology & Engineering / Electrical , Computers / Optical Data Processing , Technology & Engineering / Fire Science ,

Description:... 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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