Machine Learning Techniques for Gait Biometric Recognition (Record no. 53309)
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| 000 -LEADER | |
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| 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 |
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| -- | 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 |
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