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• W1505789425 abstract "The performance of a gait recognition method is affected by numerous challengingfactors that degrade its reliability as a behavioural biometrics for subject identification inrealistic scenario. Thus for effective visual surveillance, this thesis presents five gait recog-nition methods that address various challenging factors to reliably identify a subject inrealistic scenario with low computational complexity. It presents a gait recognition methodthat analyses spatio-temporal motion of a subject with statistical and physical parametersusing Procrustes shape analysis and elliptic Fourier descriptors (EFD). It introduces a part-based EFD analysis to achieve invariance to carrying conditions, and the use of physicalparameters enables it to achieve invariance to across-day gait variation. Although spatio-temporal deformation of a subject’s shape in gait sequences provides better discriminativepower than its kinematics, inclusion of dynamical motion characteristics improves the iden-tification rate. Therefore, the thesis presents a gait recognition method which combinesspatio-temporal shape and dynamic motion characteristics of a subject to achieve robust-ness against the maximum number of challenging factors compared to related state-of-the-art methods. A region-based gait recognition method that analyses a subject’s shape inimage and feature spaces is presented to achieve invariance to clothing variation and carry-ing conditions. To take into account of arbitrary moving directions of a subject in realisticscenario, a gait recognition method must be robust against variation in view. Hence, the the-sis presents a robust view-invariant multiscale gait recognition method. Finally, the thesisproposes a gait recognition method based on low spatial and low temporal resolution videosequences captured by a CCTV. The computational complexity of each method is analysed.Experimental analyses on public datasets demonstrate the efficacy of the proposed methods." @default.
• W1505789425 created "2016-06-24" @default.
• W1505789425 creator A5073079042 @default.
• W1505789425 date "2013-10-01" @default.
• W1505789425 modified "2023-09-27" @default.
• W1505789425 title "Uniscale and multiscale gait recognition in realistic scenario" @default.
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