Conference paper Open Access

FaceRec: An Interactive Framework for Face Recognition in Video Archives

Pasquale Lisena; Jorma Laaksonen; Raphael Troncy


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  <identifier identifierType="DOI">10.5281/zenodo.4764633</identifier>
  <creators>
    <creator>
      <creatorName>Pasquale Lisena</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3094-5585</nameIdentifier>
      <affiliation>EURECOM</affiliation>
    </creator>
    <creator>
      <creatorName>Jorma Laaksonen</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7218-3131</nameIdentifier>
      <affiliation>Aalto University</affiliation>
    </creator>
    <creator>
      <creatorName>Raphael Troncy</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0457-1436</nameIdentifier>
      <affiliation>EURECOM</affiliation>
    </creator>
  </creators>
  <titles>
    <title>FaceRec: An Interactive Framework for Face Recognition in Video Archives</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Face recognition</subject>
    <subject>Neural networks</subject>
    <subject>Semantic metadata</subject>
    <subject>Video archives</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-06-21</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4764633</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4764632</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/datatv2021</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Annotating the visual presence of a known person in a video is a hard and costly task, in particular when applied to large video corpora. The web is a massive source of visual information that can be exploited for detecting celebrities. In this work, we introduce FaceRec, an AI-based system for automatically detecting faces of known but also unknown people in a video. The system relies on a combination of state-of-the-art algorithms (MTCNN and FaceNet), applied on images crawled from web search engines. A tracking system links consecutive detection in order to adjust and correct the label predictions using a confidence-based voting mechanism. Furthermore, we add a clustering algorithm for the unlabelled faces, thus increasing the number of people that can be recognized. We evaluate our system that obtained high precision on datasets of both historical and recent videos. We release the complete framework as open-source at https://git.io/facerec .&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Agence Nationale de la Recherche</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100001665</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/ANR//ANR-17-CE38-0010/">ANR-17-CE38-0010</awardNumber>
      <awardTitle>Transdisciplinary Analysis of French Newsreels (1945-1969)</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/780069/">780069</awardNumber>
      <awardTitle>Methods for Managing Audiovisual Data: Combining  Automatic Efficiency with Human Accuracy</awardTitle>
    </fundingReference>
  </fundingReferences>
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