| 000 | 03310nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-29088-1 | ||
| 003 | DE-He213 | ||
| 005 | 20200420221303.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 160204s2016 gw | s |||| 0|eng d | ||
| 020 |
_a9783319290881 _9978-3-319-29088-1 |
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| 024 | 7 |
_a10.1007/978-3-319-29088-1 _2doi |
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| 050 | 4 | _aTK5102.9 | |
| 050 | 4 | _aTA1637-1638 | |
| 050 | 4 | _aTK7882.S65 | |
| 072 | 7 |
_aTTBM _2bicssc |
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_aUYS _2bicssc |
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_aTEC008000 _2bisacsh |
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| 072 | 7 |
_aCOM073000 _2bisacsh |
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| 082 | 0 | 4 |
_a621.382 _223 |
| 100 | 1 |
_aMason, James Eric. _eauthor. |
|
| 245 | 1 | 0 |
_aMachine Learning Techniques for Gait Biometric Recognition _h[electronic resource] : _bUsing the Ground Reaction Force / _cby James Eric Mason, Issa Traor�e, Isaac Woungang. |
| 250 | _a1st ed. 2016. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
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| 300 |
_aXXXIV, 223 p. 76 illus., 73 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aIntroduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion. | |
| 520 | _aThis 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. | ||
| 650 | 0 | _aEngineering. | |
| 650 | 0 | _aBiometrics (Biology). | |
| 650 | 0 | _aSystem safety. | |
| 650 | 1 | 4 | _aEngineering. |
| 650 | 2 | 4 | _aSignal, Image and Speech Processing. |
| 650 | 2 | 4 | _aBiometrics. |
| 650 | 2 | 4 | _aSecurity Science and Technology. |
| 700 | 1 |
_aTraor�e, Issa. _eauthor. |
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| 700 | 1 |
_aWoungang, Isaac. _eauthor. |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319290867 |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-29088-1 |
| 912 | _aZDB-2-ENG | ||
| 942 | _cEBK | ||
| 999 |
_c53309 _d53309 |
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