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Human identification using facial comparative descriptions

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
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Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
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, Spain. 04 - 07 Jun 2013.

Record type: 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, Spain, 2013-06-04 - 2013-06-07
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 353626
URI: http://eprints.soton.ac.uk/id/eprint/353626
PURE UUID: 1cb331cf-b8c0-47d7-95f2-48a403b96679
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 12 Jun 2013 10:29
Last modified: 17 Dec 2019 02:04

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Contributors

Author: Daniel Reid
Author: Mark Nixon ORCID iD

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