Modelling the time-variant covariates for gait recognition
Veres, Galina, Nixon, Mark and Carter, John (2005) Modelling the time-variant covariates for gait recognition. In, AVBPA2005, Lecture Notes in Computer Science,, New York, USA, 20 - 22 Jul 2005. Springer, 597-606.
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Event Dates: 20-22 July 2005|
|Keywords:||gait recognition, time-variant covariates|
|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
|Date Deposited:||24 Nov 2005|
|Last Modified:||27 Mar 2014 20:04|
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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