Machine Learning Techniques for Gait Biometric Recognition (Record no. 53309)
[ view plain ]
000 -LEADER | |
---|---|
fixed length control field | 03310nam a22005415i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-319-29088-1 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200420221303.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 160204s2016 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783319290881 |
-- | 978-3-319-29088-1 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 621.382 |
100 1# - AUTHOR NAME | |
Author | Mason, James Eric. |
245 10 - TITLE STATEMENT | |
Title | Machine Learning Techniques for Gait Biometric Recognition |
Sub Title | Using the Ground Reaction Force / |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2016. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XXXIV, 223 p. 76 illus., 73 illus. in color. |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Introduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
700 1# - AUTHOR 2 | |
Author 2 | Traor�e, Issa. |
700 1# - AUTHOR 2 | |
Author 2 | Woungang, Isaac. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-29088-1 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2016. |
336 ## - | |
-- | text |
-- | txt |
-- | rdacontent |
337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
-- | cr |
-- | rdacarrier |
347 ## - | |
-- | text file |
-- | |
-- | rda |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Biometrics (Biology). |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | System safety. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal, Image and Speech Processing. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Biometrics. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Security Science and Technology. |
912 ## - | |
-- | ZDB-2-ENG |
No items available.