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Recognising Humans by Gait via Parametric Canonical Space

Huang, P.S., Harris, C.J. and Nixon, M.S. (1998) Recognising Humans by Gait via Parametric Canonical Space At Proc. of International Symposium on Engineering of Intelligence Systems. , pp. 384-389.

Record type: Conference or Workshop Item (Other)


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
Additional Information: Organisation: ICSC Address: Tenerife, Spain
Venue - Dates: Proc. of International Symposium on Engineering of Intelligence Systems, 1998-02-01
Organisations: Southampton Wireless Group


Local EPrints ID: 250003
PURE UUID: cc2d9d9a-0c4b-4391-a333-10e81d6665b2

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Date deposited: 01 Dec 1999
Last modified: 18 Jul 2017 10:44

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Author: P.S. Huang
Author: C.J. Harris
Author: M.S. Nixon

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