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Soft Biometric Recognition from Comparative Crowdsourced Annotations

Soft Biometric Recognition from Comparative Crowdsourced Annotations
Soft Biometric Recognition from Comparative Crowdsourced Annotations
Soft biometrics provide cues that enable human identification from low quality video surveillance footage. This paper discusses a new crowdsourced dataset, collecting comparative soft biometric annotations from a rich set of human annotators. We now include gender as a comparative trait, and find comparative labels are more objective and obtain more accurate measurements than previous categorical labels. Using our pragmatic dataset, we perform semantic recognition by inferring relative biometric signatures. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The experiment is guaranteed to return the correct match in the top 7% of results with 10 comparisons, or top 13% of results using just 5 sets of subject comparisons.
Martinho-Corbishley, Daniel
6dd73e5c-9a7e-41bd-b896-fb1ea9852abb
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Martinho-Corbishley, Daniel
6dd73e5c-9a7e-41bd-b896-fb1ea9852abb
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da

Martinho-Corbishley, Daniel, Nixon, Mark S. and Carter, John N. (2015) Soft Biometric Recognition from Comparative Crowdsourced Annotations. 6th International Conference on Imaging for Crime Prevention and Detection.

Record type: Conference or Workshop Item (Other)

Abstract

Soft biometrics provide cues that enable human identification from low quality video surveillance footage. This paper discusses a new crowdsourced dataset, collecting comparative soft biometric annotations from a rich set of human annotators. We now include gender as a comparative trait, and find comparative labels are more objective and obtain more accurate measurements than previous categorical labels. Using our pragmatic dataset, we perform semantic recognition by inferring relative biometric signatures. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The experiment is guaranteed to return the correct match in the top 7% of results with 10 comparisons, or top 13% of results using just 5 sets of subject comparisons.

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More information

Accepted/In Press date: 15 June 2015
Published date: 16 July 2015
Venue - Dates: 6th International Conference on Imaging for Crime Prevention and Detection, 2015-06-15
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 380275
URI: http://eprints.soton.ac.uk/id/eprint/380275
PURE UUID: 0c8d4e66-fa97-4c92-a6d8-04c8c3d6ccdb
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 18 Aug 2015 10:40
Last modified: 17 Dec 2019 02:04

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