The University of Southampton
University of Southampton Institutional Repository

Recognizing Humans by Gait using a Statistical Approach forTemporal Templates

Huang, P.S., Harris, C.J. and Nixon, M.S. (1998) Recognizing Humans by Gait using a Statistical Approach forTemporal Templates At Proc. of International Conference on Systems, Man, and Cybernetics. , pp. 4556-4561.

Record type: Conference or Workshop Item (Other)


In this paper, we propose a new approach which combines canonical space transformation (CST) with the eigenspace transformation (EST) for feature extraction of temporal templates in a gait sequence. Eigenspace transformation has been demonstrated to be a potent metric in automatic face recognition and gait analysis, but without using data analysis to increase classification capability. Our method can be used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Each temporal 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 by template matching becomes much faster and simpler. Experimental results for human gait analysis show this method is superior to eigenspace representation. As such, the combination of EST and CST is shown to be of considerable advantage in an emerging new biometric.

Full text not available from this repository.

More information

Published date: October 1998
Additional Information: Organisation: IEEE Address: La Jolla, California , USA
Venue - Dates: Proc. of International Conference on Systems, Man, and Cybernetics, 1998-10-01
Organisations: Southampton Wireless Group


Local EPrints ID: 250436
PURE UUID: 2603421d-35f0-48cf-9e9e-f28022e97814

Catalogue record

Date deposited: 30 May 2000
Last modified: 18 Jul 2017 10:42

Export record


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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.