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Extending quality and covariate analyses for gait biometrics

Extending quality and covariate analyses for gait biometrics
Extending quality and covariate analyses for gait biometrics
Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.

At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependentcovariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of thedatabase evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.

Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.

Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments.
Matovski, Darko
33c2d81d-3a4e-4163-814e-513d4f09ae5b
Matovski, Darko
33c2d81d-3a4e-4163-814e-513d4f09ae5b
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

(2013) Extending quality and covariate analyses for gait biometrics. University of Southampton, Faculty of Physical & Applied Science, Doctoral Thesis, 156pp.

Record type: Thesis (Doctoral)

Abstract

Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.

At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependentcovariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of thedatabase evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.

Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.

Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments.

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More information

Published date: January 2013
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 347751
URI: http://eprints.soton.ac.uk/id/eprint/347751
PURE UUID: 39b3a144-80c5-40ee-b735-5fe752298174
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 28 Feb 2013 12:55
Last modified: 06 Jun 2018 13:18

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