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Improving face recognition with genealogical and contextual data

Published: 26 November 2012 Publication History

Abstract

The field of genealogy has embraced the move towards digitisation, with increasingly large quantities of historical photographs being digitised in an effort to both preserve and share with a wider audience. Genealogy software is prevalent, but while many programs support photograph management, none use face recognition to assist in the identification and tagging of individuals. Genealogy is in the unique position of possessing a rich source of context in the form of a family tree, that a face recognition engine can draw information from. We aim to improve the accuracy of face recognition results within a family photograph album through the use of a filter that uses available contextual information from a given family tree. We also use measures of co-occurrence, recurrence and relative physical distance of individuals within the album to accurately predict the identity of individuals. This novel use of genealogical data as context has provided encouraging results, with a 26% improvement in accuracy at hit list size 1 and a 21% improvement at size 5 over the use of face recognition alone, when identifying 348 faces against a database of 523 faces from a challenging dataset of 173 family photographs.

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cover image ACM Other conferences
IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
November 2012
547 pages
ISBN:9781450314732
DOI:10.1145/2425836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • HRS: Hoare Research Software Ltd.
  • Google Inc.
  • Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

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Author Tags

  1. context
  2. face recognition
  3. genealogy
  4. photograph albums

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IVCNZ '12
Sponsor:
  • HRS
  • Dept. of Information Science, Univ.of Otago
IVCNZ '12: Image and Vision Computing New Zealand
November 26 - 28, 2012
Dunedin, New Zealand

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