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Model-based approaches for predicting gait changes over time

Veres, Galina, Nixon, Mark and Carter, John (2005) Model-based approaches for predicting gait changes over time At International Workshop on Pattern Recognition.

Record type: Conference or Workshop Item (Paper)

Abstract

Interest in automated biometrics continues to increase, but has little consideration of time which are especially important in surveillance and scan control. This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e. when $6$ or $12$ months have passed between recording of the gallery and the probe sets. Moreover, in some cases some extra covariates present as well. We have shown previously how recognition rates fall significantly when data is captured between lengthy time intevals. 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, we suggest predictive models of changes in gait due both to time and now to time-invariant covariates. Our extended time-dependent predictive model derives high recognition rates when time-dependent or subject-dependent covariates are added. However it is not able to cope with time-invariant covariates, therefore a new time-invariant predictive model is suggested to accommodate extra covariates. These are combined to achieve a predictive model which takes into consideration all types of covariates. A considerable improvement in recognition capability is demonstrated, showing that changes can be modelled successfully by the new approach.

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More information

Published date: 2005
Venue - Dates: International Workshop on Pattern Recognition, 2005-01-01
Keywords: gait recognition, covariates, time
Organisations: Electronics & Computer Science, IT Innovation, Southampton Wireless Group

Identifiers

Local EPrints ID: 261580
URI: http://eprints.soton.ac.uk/id/eprint/261580
PURE UUID: dac8ffdd-f17f-4f56-9e24-1ef7f053d8ca

Catalogue record

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

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