Mason, James Eric.
Machine Learning Techniques for Gait Biometric Recognition Using the Ground Reaction Force / [electronic resource] : by James Eric Mason, Issa Traoré, Isaac Woungang. - 1st ed. 2016. - XXXIV, 223 p. 76 illus., 3 illus. in color. online resource.
Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.
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.
9783319290881
10.1007/978-3-319-29088-1 doi
Signal processing.
Biometric identification.
Security systems.
Signal, Speech and Image Processing .
Biometrics.
Security Science and Technology.
TK5102.9
621.382
Machine Learning Techniques for Gait Biometric Recognition Using the Ground Reaction Force / [electronic resource] : by James Eric Mason, Issa Traoré, Isaac Woungang. - 1st ed. 2016. - XXXIV, 223 p. 76 illus., 3 illus. in color. online resource.
Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.
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.
9783319290881
10.1007/978-3-319-29088-1 doi
Signal processing.
Biometric identification.
Security systems.
Signal, Speech and Image Processing .
Biometrics.
Security Science and Technology.
TK5102.9
621.382