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The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset

The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset
The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset
This paper presents the University of Southampton Multi-Biometric Tunnel, a constrained environment that is designed with airports and other high throughput environments in mind. It is able to acquire a variety of non-contact biometrics in a non-intrusive manner. The system uses eight synchronised IEEE1394 cameras to capture gait and additional cameras to capture images from the face and one ear, as an individual walks through the tunnel. We demonstrate that it is possible to achieve a 99.6% correct classification rate and a 4.3% equal error rate without feature selection using the gait data collected from the system; comparing well with state-of-art approaches. The tunnel acquires data automatically as a subject walks through it and is designed for the collection of very large gait datasets.
biometrics, gait, face, ear, dataset, recognition, identification, volumetric
Seely, Richard David
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Samangooei, Sina
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Middleton, Lee
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
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Seely, Richard David
4d14936f-0357-4cfa-8f8c-cc91356f24b8
Samangooei, Sina
c380fb26-55d4-4b34-94e7-c92bbb26a40d
Middleton, Lee
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Seely, Richard David, Samangooei, Sina, Middleton, Lee, Carter, John and Nixon, Mark (2008) The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset. Biometrics: Theory, Applications and Systems, United States.

Record type: Conference or Workshop Item (Other)

Abstract

This paper presents the University of Southampton Multi-Biometric Tunnel, a constrained environment that is designed with airports and other high throughput environments in mind. It is able to acquire a variety of non-contact biometrics in a non-intrusive manner. The system uses eight synchronised IEEE1394 cameras to capture gait and additional cameras to capture images from the face and one ear, as an individual walks through the tunnel. We demonstrate that it is possible to achieve a 99.6% correct classification rate and a 4.3% equal error rate without feature selection using the gait data collected from the system; comparing well with state-of-art approaches. The tunnel acquires data automatically as a subject walks through it and is designed for the collection of very large gait datasets.

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More information

Published date: 28 September 2008
Additional Information: Event Dates: 29th September 2008
Venue - Dates: Biometrics: Theory, Applications and Systems, United States, 2008-09-29
Keywords: biometrics, gait, face, ear, dataset, recognition, identification, volumetric
Organisations: Vision, Learning and Control, IT Innovation

Identifiers

Local EPrints ID: 266970
URI: http://eprints.soton.ac.uk/id/eprint/266970
PURE UUID: 924550f0-c77f-4903-a0d9-e862ad24c380
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 08 Dec 2008 11:55
Last modified: 17 Dec 2019 02:04

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Contributors

Author: Richard David Seely
Author: Sina Samangooei
Author: Lee Middleton
Author: John Carter
Author: Mark Nixon ORCID iD

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