Recognising Humans by Gait via Parametric Canonical Space
Recognising Humans by Gait via Parametric Canonical Space
Eigenspace transformation (EST) based on Principal Component Analysis (PCA) has been demonstrated to be a potent metric in gait analysis, but without using data analysis to increase classification capability. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with the eigenspace transformation. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences and face classes simultaneously. Each image template is projected from high-dimensional image space to a single point in low-dimensional canonical space. In this new space the recognition of human gait becomes much simpler. Experimental results for human gait analysis show this method is superior to the eigenspace representation. The comparison of EST, CST and our approach is also shown in the results. As such, the combination of EST and CST is shown to be of considerable advantage in an emerging new biometric.
384-389
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
February 1998
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Huang, P.S., Harris, C.J. and Nixon, M.S.
(1998)
Recognising Humans by Gait via Parametric Canonical Space.
Proc. of International Symposium on Engineering of Intelligence Systems.
.
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Conference or Workshop Item
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Abstract
Eigenspace transformation (EST) based on Principal Component Analysis (PCA) has been demonstrated to be a potent metric in gait analysis, but without using data analysis to increase classification capability. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with the eigenspace transformation. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences and face classes simultaneously. Each image template is projected from high-dimensional image space to a single point in low-dimensional canonical space. In this new space the recognition of human gait becomes much simpler. Experimental results for human gait analysis show this method is superior to the eigenspace representation. The comparison of EST, CST and our approach is also shown in the results. As such, the combination of EST and CST is shown to be of considerable advantage in an emerging new biometric.
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Published date: February 1998
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Organisation: ICSC Address: Tenerife, Spain
Venue - Dates:
Proc. of International Symposium on Engineering of Intelligence Systems, 1998-02-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250003
URI: http://eprints.soton.ac.uk/id/eprint/250003
PURE UUID: cc2d9d9a-0c4b-4391-a333-10e81d6665b2
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Date deposited: 01 Dec 1999
Last modified: 09 Jan 2022 02:33
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Contributors
Author:
P.S. Huang
Author:
C.J. Harris
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