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1. Supporting Historical Photo Identification with Face Recognition and Crowdsourced Human Expertise (Extended Abstract)

   https://doi.org/10.24963/ijcai.2020/660  

   Mohanty, Vikram ; Thames, David ; Mehta, Sneha ; Luther, Kurt ( July 2020 , Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Sister Conferences Best Papers)

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   Identifying people in historical photographs is important for interpreting material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65). Millions 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 evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits.

    

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2. Are 1,000 Features Worth A Picture? Combining Crowdsourcing and Face Recognition to Identify Civil War Soldiers

   Mohanty, Vikram ; Thames, David ; Luther, Kurt ( January 2018 , AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2018))

   We introduce a web-based platform called Civil War Photo Sleuth for helping users identify unknown soldiers in portraits from the American Civil War era. Our system employs a novel person identification pipeline by leveraging the complementary strengths of crowdsourced human vision and face recognition algorithms 

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3. SleuthTalk: Identifying Historical Photos with Intelligent Shortlists, Private Collaboration, and Structured Feedback

   https://doi.org/10.1145/3462204.3482890  

   Yuan, Liling ; Mohanty, Vikram ; Luther, Kurt ( October 2021 , CSCW '21: Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing)

   Identifying people in photographs is an important task in many fields, including history, journalism, genealogy, and collecting, but accurate person identification remains challenging. Researchers especially struggle with the “last-mile problem” of historical person identification, where they must make a selection among a small number of highly similar candidates. We present SleuthTalk, a web-based collaboration tool integrated into the public website Civil War Photo Sleuth which addresses the last-mile problem in historical person identification by providing support for shortlisting potential candidates from face recognition results, private collaborative workspaces, and structured feedback. 

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4. Photo Sleuth: Combining Collective Intelligence and Computer Vision to Identify Historical Portraits

   Mohanty, Vikram ; Thames, David ; Luther, Kurt ( January 2018 , ACM Conference on Collective Intelligence (CI 2018))

   Identifying people in photographs is a critical task in a wide variety of domains, from national security [7] to journalism [14] to human rights investigations [1]. The task is also fundamentally complex and challenging. With the world population at 7.6 billion and growing, the candidate pool is large. Studies of human face recognition ability show that the average person incorrectly identifies two people as similar 20–30% of the time, and trained police detectives do not perform significantly better [11]. Computer vision-based face recognition tools have gained considerable ground and are now widely available commercially, but comparisons to human performance show mixed results at best [2,10,16]. Automated face recognition techniques, while powerful, also have constraints that may be impractical for many real-world contexts. For example, face recognition systems tend to suffer when the target image or reference images have poor quality or resolution, as blemishes or discolorations may be incorrectly recognized as false positives for facial landmarks. Additionally, most face recognition systems ignore some salient facial features, like scars or other skin characteristics, as well as distinctive non-facial features, like ear shape or hair or facial hair styles. This project investigates how we can overcome these limitations to support person identification tasks. By adjusting confidence thresholds, users of face recognition can generally expect high recall (few false negatives) at the cost of low precision (many false positives). Therefore, we focus our work on the “last mile” of person identification, i.e., helping a user find the correct match among a large set of similarlooking candidates suggested by face recognition. Our approach leverages the powerful capabilities of the human vision system and collaborative sensemaking via crowdsourcing to augment the complementary strengths of automatic face recognition. The result is a novel technology pipeline combining collective intelligence and computer vision. We scope this project to focus on identifying soldiers in photos from the American Civil War era (1861– 1865). An estimated 4,000,000 soldiers fought in the war, and most were photographed at least once, due to decreasing costs, the increasing robustness of the format, and the critical events separating friends and family [17]. Over 150 years later, the identities of most of these portraits have been lost, but as museums and archives increasingly digitize and publish their collections online, the pool of reference photos and information has never been more accessible. Historians, genealogists, and collectors work tirelessly to connect names with faces, using largely manual identification methods [3,9]. Identifying people in historical photos is important for preserving material culture [9], correcting the historical record [13], and recognizing contributions of marginalized groups [4], among other reasons. 

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5. Double-Checking History: Designing Assessable Systems for Historical Photo Identification

   Mohanty, Vikram ; Luther, Kurt ( January 2021 , AAAI HCOMP 2021 Demos)

   Identifying historical photographs of people can generate significant cultural and economic value, but misidentifications can cause harms such as falsifying the historical record, spreading disinformation, and feeding conspiracy theories. In this paper, we introduce DoubleCheck, a quality assessment framework based on the concepts of information provenance and stewardship for verifying historical photo identifications. We built and evaluated DoubleCheck on Civil War Photo Sleuth (CWPS), a popular online community dedicated to identifying photos from the American CivilWar era (1861- 65) using facial recognition and crowdsourcing. 

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