000753703 000__ 03425cam\a2200481Ii\4500
000753703 001__ 753703
000753703 005__ 20230306141515.0
000753703 006__ m\\\\\o\\d\\\\\\\\
000753703 007__ cr\cn\nnnunnun
000753703 008__ 160208t20162016sz\\\\\\ob\\\\001\0\eng\d
000753703 019__ $$a939262126
000753703 020__ $$a9783319290881$$q(electronic book)
000753703 020__ $$a3319290886$$q(electronic book)
000753703 020__ $$z9783319290867
000753703 0247_ $$a10.1007/978-3-319-29088-1$$2doi
000753703 035__ $$aSP(OCoLC)ocn937392245
000753703 035__ $$aSP(OCoLC)937392245$$z(OCoLC)939262126
000753703 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dYDXCP$$dIDEBK$$dGW5XE$$dAZU$$dOCLCF$$dEBLCP$$dDEBSZ$$dCOO
000753703 049__ $$aISEA
000753703 050_4 $$aQ325.5
000753703 08204 $$a006.31$$223
000753703 1001_ $$aMason, James Eric,$$eauthor.
000753703 24510 $$aMachine learning techniques for gait biometric recognition$$h[electronic resource] :$$busing the ground reaction force /$$cJames Eric Mason, Issa Traoré, Isaac Woungang.
000753703 264_1 $$aSwitzerland :$$bSpringer,$$c[2016]
000753703 264_4 $$c©2016
000753703 300__ $$a1 online resource.
000753703 336__ $$atext$$btxt$$2rdacontent
000753703 337__ $$acomputer$$bc$$2rdamedia
000753703 338__ $$aonline resource$$bcr$$2rdacarrier
000753703 504__ $$aIncludes bibliographical references and index.
000753703 5050_ $$aIntroduction -- Background -- Experimental Design and Dataset -- Feature Extraction.-Normalization -- Classification -- Measured Performance -- Experimental Analysis -- Conclusion.
000753703 506__ $$aAccess limited to authorized users.
000753703 520__ $$aThis book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition · provides detailed discussions of key research challenges and open research issues in gait biometrics recognition · compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear.
000753703 588__ $$aOnline resource; title from PDF title page (viewed February 10, 2016).
000753703 650_0 $$aMachine learning.
000753703 650_0 $$aBiometric identification.
000753703 7001_ $$aTraoré, Issa,$$eauthor.
000753703 7001_ $$aWoungang, Isaac,$$eauthor.
000753703 77608 $$iPrint version:$$aMason, James Eric$$tMachine Learning Techniques for Gait Biometric Recognition : Using the Ground Reaction Force$$dCham : Springer International Publishing,c2016$$z9783319290867
000753703 852__ $$bebk
000753703 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-29088-1$$zOnline Access$$91397441.1
000753703 909CO $$ooai:library.usi.edu:753703$$pGLOBAL_SET
000753703 980__ $$aEBOOK
000753703 980__ $$aBIB
000753703 982__ $$aEbook
000753703 983__ $$aOnline
000753703 994__ $$a92$$bISE