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Human Gait Recognition in Canonical Space Using Temporal Templates

Human Gait Recognition in Canonical Space Using Temporal Templates
Human Gait Recognition in Canonical Space Using Temporal Templates
This paper describes a system for automatic gait recognition without segmentation of particular body parts. Eigenspace transformation (EST) has already proved useful for several tasks including face recognition, gait analysis, etc. However, EST is optimal in dimensionality reduction by maximising the total scatter of all classes but is not optimal for class separability. In this paper, a statistical approach which combines EST with canonical space transformation (CST) is proposed for gait recognition using temporal templates from a gait sequence as features. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences simultaneously. Incorporating temporal information from optical-flow changes between two consecutive spatial templates, each temporal template extracted from computation of optical flow is projected from a high-dimensional image space to a single point in a low-dimensional canonical space. Using template matching, recognition of human gait becomes much faster and simpler in this new space. As such, the combination of EST and CST is shown to be of considerable potential in an emerging new biometric.
93-100
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Huang, P.S., Harris, C.J. and Nixon, Mark S. (1999) Human Gait Recognition in Canonical Space Using Temporal Templates IEE Proceedings - Vision, Image and Signal Processing, 146, (2), pp. 93-100.

Record type: Article

Abstract

This paper describes a system for automatic gait recognition without segmentation of particular body parts. Eigenspace transformation (EST) has already proved useful for several tasks including face recognition, gait analysis, etc. However, EST is optimal in dimensionality reduction by maximising the total scatter of all classes but is not optimal for class separability. In this paper, a statistical approach which combines EST with canonical space transformation (CST) is proposed for gait recognition using temporal templates from a gait sequence as features. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences simultaneously. Incorporating temporal information from optical-flow changes between two consecutive spatial templates, each temporal template extracted from computation of optical flow is projected from a high-dimensional image space to a single point in a low-dimensional canonical space. Using template matching, recognition of human gait becomes much faster and simpler in this new space. As such, the combination of EST and CST is shown to be of considerable potential in an emerging new biometric.

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More information

Published date: 1999
Additional Information: to be published
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250440
URI: http://eprints.soton.ac.uk/id/eprint/250440
PURE UUID: 83ce4c44-0b70-4d75-ba66-e2174da52aa6

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Date deposited: 18 Nov 1999
Last modified: 18 Jul 2017 10:41

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Contributors

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

University divisions

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