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Automatic Gait Recognition via the Generalised Symmetry Operator

Automatic Gait Recognition via the Generalised Symmetry Operator
Automatic Gait Recognition via the Generalised Symmetry Operator
We describe a new method for automatic gait recognition based on analysing the symmetry of human motion, by using the Generalised Symmetry Operator. This operator, rather than relying on the borders of a shape or on its general appearance, is able to locate features by their symmetrical properties. Essentially, it accumulates the symmetries between image points to give a symmetry map. This approach is reinforced by the psychologists' view that gait is a symmetrical pattern of motion. It is also supported by work that suggests pendular motion is an appropriate model for automatic gait recognition. The Fourier transform is used to derive the gait signatures from the symmetry map, in view of its invariance properties and its descriptive capability. We applied our new method to two different databases of four and seven sequences of four and of six subjects, respectively. For both databases, we derived gait signatures for silhouette and optic flow information. The results show that the symmetry properties of individuals' gait appear to be unique and can indeed be used for recognition. We have so far achieved a Correct Classification Rate exceeding 95% by the nearest neighbour rule with k=1 and k=3, and that is a very promising start. The performance analysis also suggests that symmetry enjoys practical advantages in recognition such as relative immunity to noise and missing frames, with capability to handle occlusion
Hayfron-Acquah, James B.
bffee551-1655-495a-92fd-8c548dcef084
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
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Hayfron-Acquah, James B.
bffee551-1655-495a-92fd-8c548dcef084
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da

Hayfron-Acquah, James B., Nixon, Mark S. and Carter, John N. (2001) Automatic Gait Recognition via the Generalised Symmetry Operator. BMVA Workshop Understanding Visual Behaviour.

Record type: Conference or Workshop Item (Poster)

Abstract

We describe a new method for automatic gait recognition based on analysing the symmetry of human motion, by using the Generalised Symmetry Operator. This operator, rather than relying on the borders of a shape or on its general appearance, is able to locate features by their symmetrical properties. Essentially, it accumulates the symmetries between image points to give a symmetry map. This approach is reinforced by the psychologists' view that gait is a symmetrical pattern of motion. It is also supported by work that suggests pendular motion is an appropriate model for automatic gait recognition. The Fourier transform is used to derive the gait signatures from the symmetry map, in view of its invariance properties and its descriptive capability. We applied our new method to two different databases of four and seven sequences of four and of six subjects, respectively. For both databases, we derived gait signatures for silhouette and optic flow information. The results show that the symmetry properties of individuals' gait appear to be unique and can indeed be used for recognition. We have so far achieved a Correct Classification Rate exceeding 95% by the nearest neighbour rule with k=1 and k=3, and that is a very promising start. The performance analysis also suggests that symmetry enjoys practical advantages in recognition such as relative immunity to noise and missing frames, with capability to handle occlusion

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More information

Published date: January 2001
Additional Information: http://www.bmva.ac.uk/meetings/meetings/01/24jan01/index.html. Organisation: British Machine Vision Association
Venue - Dates: BMVA Workshop Understanding Visual Behaviour, 2001-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 254253
URI: http://eprints.soton.ac.uk/id/eprint/254253
PURE UUID: cffb7b24-6f95-4afd-8e06-67df7b218bd2
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 20 Nov 2003
Last modified: 30 Jan 2020 01:24

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Contributors

Author: James B. Hayfron-Acquah
Author: Mark S. Nixon ORCID iD
Author: John N. Carter

University divisions

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