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 intervals, 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
2005
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,, New York, United States.
20 - 22 Jul 2005.
.
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 intervals, 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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Published date: 2005
Additional Information:
Event Dates: 20-22 July 2005
Venue - Dates:
AVBPA2005, Lecture Notes in Computer Science,, New York, 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: 15 Mar 2024 02:35
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Contributors
Author:
Galina Veres
Author:
John Carter
Editor:
T Kanade
Editor:
A.K. Jain
Editor:
N.K. Ratha
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