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Photo Sleuth: Identifying Historical Portraits with Face Recognition and Crowdsourced Human Expertise

Published: 16 October 2020 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on October 22, 2020. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

Abstract

Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this article, we focus on identifying portraits of soldiers who participated in the American Civil War (1861--65), the first widely photographed conflict. Many thousands of these portraits survive, but only 10%--20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluations of Photo Sleuth one month and 11 months after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.

Supplementary Material

3365842-VoR (3365842-vor.pdf)
Version of Record for "Photo Sleuth: Identifying Historical Portraits with Face Recognition and Crowdsourced Human Expertise" by Mohanty et al., ACM Transactions on Interactive Intelligent Systems, Volume 10, Issue 4 (TIIS 10:4).

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cover image ACM Transactions on Interactive Intelligent Systems
ACM Transactions on Interactive Intelligent Systems  Volume 10, Issue 4
Special Issue on IUI 2019 Highlights
December 2020
274 pages
ISSN:2160-6455
EISSN:2160-6463
DOI:10.1145/3430697
Issue’s Table of Contents
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Publication History

Published: 16 October 2020
Accepted: 01 October 2019
Revised: 01 October 2019
Received: 01 July 2019
Published in TIIS Volume 10, Issue 4

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Author Tags

  1. Crowdsourcing
  2. crowd-AI interaction
  3. face recognition
  4. history
  5. online communities
  6. person identification

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  • Refereed

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  • NSF
  • Virginia Tech ICTAS Junior Faculty Award

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