Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572752
Title: Human identification using soft biometrics
Author: Reid, Daniel
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2013
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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.
Supervisor: Nixon, Mark Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.572752  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
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