| Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force Softcover Repri Edition Contributor(s): Mason, James Eric (Author), Traoré, Issa (Author), Woungang, Isaac (Author) |
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ISBN: 3319804863 ISBN-13: 9783319804866 Publisher: Springer OUR PRICE: $52.24 Product Type: Paperback - Other Formats Published: March 2018 |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Electronics - General - Computers | Computer Vision & Pattern Recognition - Technology & Engineering | Engineering (general) |
| Dewey: 570.151 |
| Physical Information: 0.55" H x 6.14" W x 9.21" (0.81 lbs) 223 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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 |