Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595747
Title: Uniscale and multiscale gait recognition in realistic scenario
Author: Das Choudhury, Sruti
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2013
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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.
Supervisor: Not available Sponsor: University of Warwick
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.595747  DOI: Not available
Keywords: QH301 Biology ; TA Engineering (General). Civil engineering (General)
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