On including quality in applied automatic gait recognition
On including quality in applied automatic gait recognition
Many gait recognition approaches use silhouette data. Imperfections in silhouette extraction have a negative effect on the performance of a gait recognition system. In this paper we extend quality metrics for gait recognition and evaluate new ways of using quality to improve a recognition system. We demonstrate use of quality to improve silhouette data and select gait cycles of best quality. The potential of the new approaches has been demonstrated experimentally on a challenging dataset, showing how recognition capability can be dramatically improved. Our practical study also shows that acquiring samples of adequate quality in arbitrary environments is difficult and that including quality analysis can improve performance markedly.
Matovski, D.
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Nixon, M.
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Mahmoodi, S.
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Mansfield, T.
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July 2012
Matovski, D.
ca7e093f-3eae-49b0-a0b8-6fd511944e00
Nixon, M.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mahmoodi, S.
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Mansfield, T.
2986ec15-073a-43c8-bb02-145c031e70be
Matovski, D., Nixon, M., Mahmoodi, S. and Mansfield, T.
(2012)
On including quality in applied automatic gait recognition.
21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan.
11 - 15 Nov 2012.
4 pp
.
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Conference or Workshop Item
(Paper)
Abstract
Many gait recognition approaches use silhouette data. Imperfections in silhouette extraction have a negative effect on the performance of a gait recognition system. In this paper we extend quality metrics for gait recognition and evaluate new ways of using quality to improve a recognition system. We demonstrate use of quality to improve silhouette data and select gait cycles of best quality. The potential of the new approaches has been demonstrated experimentally on a challenging dataset, showing how recognition capability can be dramatically improved. Our practical study also shows that acquiring samples of adequate quality in arbitrary environments is difficult and that including quality analysis can improve performance markedly.
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e-pub ahead of print date: July 2012
Published date: July 2012
Venue - Dates:
21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 2012-11-11 - 2012-11-15
Organisations:
Vision, Learning and Control
Identifiers
Local EPrints ID: 340621
URI: http://eprints.soton.ac.uk/id/eprint/340621
PURE UUID: ebaacd26-807a-4156-88c2-5f3ad1e8c437
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Date deposited: 26 Jun 2012 14:07
Last modified: 15 Mar 2024 02:35
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Contributors
Author:
D. Matovski
Author:
S. Mahmoodi
Author:
T. Mansfield
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