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Modelling the time-variant covariates for gait recognition

Record type: Conference or Workshop Item (Paper)

This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e. when $6$ months have passed between recording of the gallery and the probe sets. We show how recognition rates fall significantly when data is captured between lengthy time intevals, for static and dynamic gait features. Under the assumption that it is possible to have some subjects from the probe for training and that similar subjects have similar changes in gait over time, a predictive model of changes in gait is suggested in this paper, which can improve the recognition capability. A small number of subjects were used for training and a much large number for classification and the probe contains the covariate data for a smaller number of subjects. Our new predictive model derives high recognition rates for different features which is a considerable improvement on recognition capability without this new approach.

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Citation

Veres, Galina, Nixon, Mark and Carter, John, (2005) Modelling the time-variant covariates for gait recognition Kanade, T, Jain, A.K. and Ratha, N.K. (eds.) At AVBPA2005, Lecture Notes in Computer Science,, United States. 20 - 22 Jul 2005. , pp. 597-606.

More information

Published date: 2005
Additional Information: Event Dates: 20-22 July 2005
Venue - Dates: AVBPA2005, Lecture Notes in Computer Science,, United States, 2005-07-20 - 2005-07-22
Keywords: gait recognition, time-variant covariates
Organisations: Electronics & Computer Science, IT Innovation, Southampton Wireless Group

Identifiers

Local EPrints ID: 261578
URI: http://eprints.soton.ac.uk/id/eprint/261578
ISBN: 3-540-27887-7
PURE UUID: cad72db7-17e8-4371-9240-ab889a11da24

Catalogue record

Date deposited: 24 Nov 2005
Last modified: 18 Jul 2017 09:01

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Contributors

Author: Galina Veres
Author: Mark Nixon
Author: John Carter
Editor: T Kanade
Editor: A.K. Jain
Editor: N.K. Ratha

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