Human identification using facial comparative descriptions
Human identification using facial comparative descriptions
Eyewitness descriptions are vital for many criminal investigations, although typically still require manual discovery of possible suspects. Soft biometrics introduce a possibility to automatically search databases based on biometric features obtained from verbal descriptions. In this paper we introduce the use of comparative human descriptions for facial identification. Twenty-seven comparative traits are used to accurately describe facial features. The Elo rating system is utilized to determine continuous biometric features from multiple comparative descriptions. Experiments on the Soton gait database demonstrate a 96.7% identification accuracy with just three comparisons.
Reid, Daniel
2a5d60ee-542b-45fb-82c8-6bf1189696b8
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
5 June 2013
Reid, Daniel
2a5d60ee-542b-45fb-82c8-6bf1189696b8
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Reid, Daniel and Nixon, Mark
(2013)
Human identification using facial comparative descriptions.
International Conference on Biometrics, Madrid, Spain.
04 - 07 Jun 2013.
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Conference or Workshop Item
(Paper)
Abstract
Eyewitness descriptions are vital for many criminal investigations, although typically still require manual discovery of possible suspects. Soft biometrics introduce a possibility to automatically search databases based on biometric features obtained from verbal descriptions. In this paper we introduce the use of comparative human descriptions for facial identification. Twenty-seven comparative traits are used to accurately describe facial features. The Elo rating system is utilized to determine continuous biometric features from multiple comparative descriptions. Experiments on the Soton gait database demonstrate a 96.7% identification accuracy with just three comparisons.
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Published date: 5 June 2013
Venue - Dates:
International Conference on Biometrics, Madrid, Spain, 2013-06-04 - 2013-06-07
Organisations:
Vision, Learning and Control
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Local EPrints ID: 353626
URI: http://eprints.soton.ac.uk/id/eprint/353626
PURE UUID: 1cb331cf-b8c0-47d7-95f2-48a403b96679
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Date deposited: 12 Jun 2013 10:29
Last modified: 15 Mar 2024 02:35
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Author:
Daniel Reid
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