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Human Perambulation as a Self Calibrating Biometric

Human Perambulation as a Self Calibrating Biometric
Human Perambulation as a Self Calibrating Biometric
This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios.
Human motion analysis, 3D modeling, Gait, Biometrics
0302-9743 (Print) 1611-3349 (Online)
139-153
Springer Berlin / Heidelberg
Goffredo, Michela
21a346d2-8ce6-46b7-883f-89a2c584afc7
Spencer, Nicholas
f6334472-fe21-467c-a515-6b6777bd32e9
Pearce, Daniel
f9edbe8b-956d-448f-93a2-790cd458d5d2
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Goffredo, Michela
21a346d2-8ce6-46b7-883f-89a2c584afc7
Spencer, Nicholas
f6334472-fe21-467c-a515-6b6777bd32e9
Pearce, Daniel
f9edbe8b-956d-448f-93a2-790cd458d5d2
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Goffredo, Michela, Spencer, Nicholas, Pearce, Daniel, Carter, John and Nixon, Mark (2007) Human Perambulation as a Self Calibrating Biometric. In, Lecture Notes in Computer Science - Analysis and Modeling of Faces and Gestures. Springer Berlin / Heidelberg, pp. 139-153.

Record type: Book Section

Abstract

This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios.

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Published date: 4 November 2007
Keywords: Human motion analysis, 3D modeling, Gait, Biometrics
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265171
URI: http://eprints.soton.ac.uk/id/eprint/265171
ISBN: 0302-9743 (Print) 1611-3349 (Online)
PURE UUID: 8389957e-d84c-49de-b765-1a0dd813bac0
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 12 Feb 2008 16:52
Last modified: 17 Dec 2019 02:04

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Contributors

Author: Michela Goffredo
Author: Nicholas Spencer
Author: Daniel Pearce
Author: John Carter
Author: Mark Nixon ORCID iD

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