On a three-dimensional gait recognition system
On a three-dimensional gait recognition system
The University of Southampton Multi-Biometric Tunnel is a high performance data-capture and recognition system; designed with airports and other busy public areas in mind. It is able to acquire a variety of non-contact biometrics in a non-intrusive manner, requiring minimal subject cooperation. The system uses twelve cameras to record gait and perform three-dimensional reconstruction; the use of volumetric data avoids the problems caused by viewpoint dependence - a serious problem for many gait analysis approaches.
The early prototype by Middleton et al. was used as the basis for creating a new and improved system, designed for the collection of a new large dataset, containing gait, face and ear. Extensive modifications were made, including new software for managing the data collection experiment and processing the dataset. Rigorous procedures were implemented to protect the privacy of participants and ensure consistency between capture sessions. Collection of the new multi-biometric dataset spanned almost one year; resulting in over 200 subjects and 2000 samples.
Experiments performed on the newly collected dataset resulted in excellent recognition performance, with all samples correctly classified and a 1.58% equal error rate; the matching of subjects against previous samples was also found to be reasonably accurate. The fusion of gait with a simple facial analysis technique found the addition of gait to be beneficial -- especially at a distance. Further experiments investigated the effect of static and dynamic features, camera misalignment, average silhouette resolution, camera layout, and the matching of outdoor video footage against data from the Biometric Tunnel. The results in this thesis prove significant due to the unprecedented size of the new dataset and the excellent recognition performance achieved; providing a significant body of evidence to support the argument that an individual's gait is unique.
L. Middleton, D. K. Wagg, A. I. Bazin, J. N. Carter and M. S. Nixon. A smart environment for biometric capture. Automation Science and Engineering, Proceedings of IEEE
International Conference on, 57-62, 2006.
Seely, Richard D.
5facdec8-59d4-48ee-971a-ee1219b3608c
July 2010
Seely, Richard D.
5facdec8-59d4-48ee-971a-ee1219b3608c
Carter, John
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Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Seely, Richard D.
(2010)
On a three-dimensional gait recognition system.
University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 161pp.
Record type:
Thesis
(Doctoral)
Abstract
The University of Southampton Multi-Biometric Tunnel is a high performance data-capture and recognition system; designed with airports and other busy public areas in mind. It is able to acquire a variety of non-contact biometrics in a non-intrusive manner, requiring minimal subject cooperation. The system uses twelve cameras to record gait and perform three-dimensional reconstruction; the use of volumetric data avoids the problems caused by viewpoint dependence - a serious problem for many gait analysis approaches.
The early prototype by Middleton et al. was used as the basis for creating a new and improved system, designed for the collection of a new large dataset, containing gait, face and ear. Extensive modifications were made, including new software for managing the data collection experiment and processing the dataset. Rigorous procedures were implemented to protect the privacy of participants and ensure consistency between capture sessions. Collection of the new multi-biometric dataset spanned almost one year; resulting in over 200 subjects and 2000 samples.
Experiments performed on the newly collected dataset resulted in excellent recognition performance, with all samples correctly classified and a 1.58% equal error rate; the matching of subjects against previous samples was also found to be reasonably accurate. The fusion of gait with a simple facial analysis technique found the addition of gait to be beneficial -- especially at a distance. Further experiments investigated the effect of static and dynamic features, camera misalignment, average silhouette resolution, camera layout, and the matching of outdoor video footage against data from the Biometric Tunnel. The results in this thesis prove significant due to the unprecedented size of the new dataset and the excellent recognition performance achieved; providing a significant body of evidence to support the argument that an individual's gait is unique.
L. Middleton, D. K. Wagg, A. I. Bazin, J. N. Carter and M. S. Nixon. A smart environment for biometric capture. Automation Science and Engineering, Proceedings of IEEE
International Conference on, 57-62, 2006.
More information
Published date: July 2010
Organisations:
University of Southampton
Identifiers
Local EPrints ID: 159881
URI: http://eprints.soton.ac.uk/id/eprint/159881
PURE UUID: 32e02bc6-ac76-4126-97a4-b1bb6b848f06
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Date deposited: 15 Jul 2010 15:49
Last modified: 14 Mar 2024 02:32
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Contributors
Author:
Richard D. Seely
Thesis advisor:
John Carter
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