Recognising humans by gait via parametric canonical space
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), 359-366.
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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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||18 Nov 1999|
|Last Modified:||02 Mar 2012 11:37|
|Contributors:||Huang, P.S. (Author)
Harris, C.J. (Author)
Nixon, M.S. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||59|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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