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Modelling the time-variant covariates for gait recognition

Modelling the time-variant covariates for gait recognition
Modelling the time-variant covariates for gait recognition
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.
gait recognition, time-variant covariates
3-540-27887-7
597-606
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Kanade, T
179c5324-3ab4-47f4-a7d5-b3ce4981a73e
Jain, A.K.
6a381a3e-f3f4-4abc-9210-1be37a0be249
Ratha, N.K.
f0f9f8b5-7299-49fd-83a1-17456e7a9249
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Kanade, T
179c5324-3ab4-47f4-a7d5-b3ce4981a73e
Jain, A.K.
6a381a3e-f3f4-4abc-9210-1be37a0be249
Ratha, N.K.
f0f9f8b5-7299-49fd-83a1-17456e7a9249

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.) AVBPA2005, Lecture Notes in Computer Science,, United States. 20 - 22 Jul 2005. pp. 597-606 .

Record type: Conference or Workshop Item (Paper)

Abstract

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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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: https://eprints.soton.ac.uk/id/eprint/261578
ISBN: 3-540-27887-7
PURE UUID: cad72db7-17e8-4371-9240-ab889a11da24
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 24 Nov 2005
Last modified: 06 Jun 2018 13:17

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Contributors

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

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