Recognising humans by gait via parametric canonical space
Recognising humans by gait via parametric canonical space
Based on Principal Component Analysis (PCA), eigenspace transformation (EST) has been demonstrated to be a potent metric in automatic face recognition and gait analysis by template matching, but without using data analysis to increase classification capability. Gait is a new biometric aimed to recognise subjects by the way they walk. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with eigenspace transformation for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously. Each image template is projected from the high-dimensional image space to a low-dimensional canonical space. Using template matching, recognition of human gait becomes much more accurate and robust in this new space. Example results on a small database show how subjects can be recognised with 100% accuracy by their gait, using this method.
359-366
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
November 1999
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.
(1999)
Recognising humans by gait via parametric canonical space.
Journal of Artificial Intelligence in Engineering, 13 (4), .
Abstract
Based on Principal Component Analysis (PCA), eigenspace transformation (EST) has been demonstrated to be a potent metric in automatic face recognition and gait analysis by template matching, but without using data analysis to increase classification capability. Gait is a new biometric aimed to recognise subjects by the way they walk. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with eigenspace transformation for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously. Each image template is projected from the high-dimensional image space to a low-dimensional canonical space. Using template matching, recognition of human gait becomes much more accurate and robust in this new space. Example results on a small database show how subjects can be recognised with 100% accuracy by their gait, using this method.
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Published date: November 1999
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250439
URI: http://eprints.soton.ac.uk/id/eprint/250439
PURE UUID: 691696c1-ad1e-42cb-9a26-5e3808c7d6ad
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Date deposited: 18 Nov 1999
Last modified: 09 Jan 2022 02:33
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Author:
P.S. Huang
Author:
C.J. Harris
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