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Uniscale and multiscale gait recognition in realistic scenario
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Das Choudhury, Sruti (2013) Uniscale and multiscale gait recognition in realistic scenario. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b2703775~S1
Abstract
The performance of a gait recognition method is affected by numerous challenging
factors that degrade its reliability as a behavioural biometrics for subject identification in
realistic scenario. Thus for effective visual surveillance, this thesis presents five gait recog-
nition methods that address various challenging factors to reliably identify a subject in
realistic scenario with low computational complexity. It presents a gait recognition method
that analyses spatio-temporal motion of a subject with statistical and physical parameters
using 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 physical
parameters enables it to achieve invariance to across-day gait variation. Although spatio-
temporal deformation of a subject’s shape in gait sequences provides better discriminative
power than its kinematics, inclusion of dynamical motion characteristics improves the iden-
tification rate. Therefore, the thesis presents a gait recognition method which combines
spatio-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 in
image 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 realistic
scenario, 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 thesis
proposes a gait recognition method based on low spatial and low temporal resolution video
sequences captured by a CCTV. The computational complexity of each method is analysed.
Experimental analyses on public datasets demonstrate the efficacy of the proposed methods.
Item Type: | Thesis (PhD) |
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Subjects: | Q Science > QH Natural history > QH301 Biology T Technology > TA Engineering (General). Civil engineering (General) |
Library of Congress Subject Headings (LCSH): | Biometry, Gait in humans -- Mathematical models, Pattern recognition systems, Fourier analysis, Computer vision |
Official Date: | October 2013 |
Institution: | University of Warwick |
Theses Department: | School of Engineering |
Thesis Type: | PhD |
Publication Status: | Unpublished |
Supervisor(s)/Advisor: | Tjahjadi, Tardi |
Sponsors: | Warwick Postgraduate Research Scholarship (WPRS); University of Warwick. Department of Engineering |
Extent: | xx,163 leaves : charts. |
Language: | eng |
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