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. In, International Workshop on Pattern Recognition

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Description/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.

Item Type: Conference or Workshop Item (Paper)
Keywords: gait recognition, covariates, time
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
Faculty of Physical Sciences and Engineering > Electronics and Computer Science
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > IT Innovation Centre
ePrint ID: 261580
Date Deposited: 24 Nov 2005
Last Modified: 27 Mar 2014 20:04
Further Information:Google Scholar
ISI Citation Count:0
URI: http://eprints.soton.ac.uk/id/eprint/261580

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