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
024	7		_a10.1007/978-3-319-29088-1 _2doi
050		4	_aTK5102.9
050		4	_aTA1637-1638
050		4	_aTK7882.S65
072		7	_aTTBM _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aCOM073000 _2bisacsh
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.
300			_aXXXIV, 223 p. 76 illus., 73 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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.
700	1		_aWoungang, Isaac. _eauthor.
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