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Human identification using soft biometrics

Human identification using soft biometrics
Human identification using soft biometrics
Humans naturally use descriptions to verbally convey the appearance of an individual. Eyewitness descriptions are an important resource for many criminal investigations. However, they cannot be used to automatically search databases featuring video or biometric data - reducing the utility of human descriptions in the search for the suspect. Soft biometrics are a new form of biometric identification which uses physical or behavioural traits that can be naturally described by humans. This thesis will explore how soft biometrics can be used alongside traditional biometrics, allowing video footage and biometric data to be searched using a description.

To permit soft biometric identification the human description must be accurate, yet conventional descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe the differences between subjects. A database of facial and bodily comparative labels is introduced and analysed.

Prior to use as a biometric feature, comparative descriptions must be anchored. Several techniques to convert multiple comparative labels into a single relative measurement are explored. Recognition experiments were conducted to assess the discriminative capabilities of relative measurements as a biometric.

Relative measurements can also be obtained from other forms of human representation. This is demonstrated using several machine learning techniques to determine relative measurements from gait biometric signatures. Retrieval results are presented showing the ability to automatically search video footage using comparative descriptions.
Reid, Daniel
2a5d60ee-542b-45fb-82c8-6bf1189696b8
Reid, Daniel
2a5d60ee-542b-45fb-82c8-6bf1189696b8
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

(2013) Human identification using soft biometrics. University of Southampton, Faculty of Physical Sciences & Engineering, Doctoral Thesis, 115pp.

Record type: Thesis (Doctoral)

Abstract

Humans naturally use descriptions to verbally convey the appearance of an individual. Eyewitness descriptions are an important resource for many criminal investigations. However, they cannot be used to automatically search databases featuring video or biometric data - reducing the utility of human descriptions in the search for the suspect. Soft biometrics are a new form of biometric identification which uses physical or behavioural traits that can be naturally described by humans. This thesis will explore how soft biometrics can be used alongside traditional biometrics, allowing video footage and biometric data to be searched using a description.

To permit soft biometric identification the human description must be accurate, yet conventional descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe the differences between subjects. A database of facial and bodily comparative labels is introduced and analysed.

Prior to use as a biometric feature, comparative descriptions must be anchored. Several techniques to convert multiple comparative labels into a single relative measurement are explored. Recognition experiments were conducted to assess the discriminative capabilities of relative measurements as a biometric.

Relative measurements can also be obtained from other forms of human representation. This is demonstrated using several machine learning techniques to determine relative measurements from gait biometric signatures. Retrieval results are presented showing the ability to automatically search video footage using comparative descriptions.

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More information

Published date: April 2013
Organisations: University of Southampton, Southampton Wireless Group

Identifiers

Local EPrints ID: 352293
URI: http://eprints.soton.ac.uk/id/eprint/352293
PURE UUID: f2adb88a-c7aa-472c-a8e9-bc4c6368ccb9
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 09 May 2013 10:42
Last modified: 06 Jun 2018 13:18

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Contributors

Author: Daniel Reid
Thesis advisor: Mark Nixon ORCID iD

University divisions

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