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
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Carter, John
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Nixon, Mark
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28 September 2008
Seely, Richard David
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Samangooei, Sina
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Middleton, Lee
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Carter, John
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Nixon, Mark
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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, Hyatt Regency Crystal City, Washington DC, United States.
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Conference or Workshop Item
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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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Published date: 28 September 2008
Additional Information:
Event Dates: 29th September 2008
Venue - Dates:
Biometrics: Theory, Applications and Systems, Hyatt Regency Crystal City, Washington DC, 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
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Date deposited: 08 Dec 2008 11:55
Last modified: 15 Mar 2024 02:35
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Contributors
Author:
Richard David Seely
Author:
Sina Samangooei
Author:
Lee Middleton
Author:
John Carter
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