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2021
Conference Paper
Titel

MFR 2021: Masked Face Recognition Competition

Abstract
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.
Author(s)
Boutros, Fadi
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kolf, Jan Niklas
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Raja, Kiran
Norwegian Univ. of Science and Technology
Kirchbuchner, Florian orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Ramachandra, Raghavendra
Norwegian Univ. of Science and Technology
Kuijper, Arjan orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Fang, Pengcheng
TYAI, China
Zhang, Chao
TYAI, China
Wang, Fei
TYAI, China
Montero, David
VICOMTECH / Univ. of the Basque Country, Spain
Aginako, Naiara
Univ. of the Basque Country, Spain
Sierra, Basilio
Univ. of the Basque Country, Spain
Nieto, Marcos
VICOMTECH, Spain
Erakin, Mustafa Ekrem
Istanbul Technical Univ.
Demir, Ugur
Istanbul Technical Univ.
Ekenel, Hazim Kemal
Istanbul Technical Univ.
Kataoka, Asaki
ACES, Inc, Japan
Ichikawa, Kohei
ACES, Inc, Japan
Kubo, Shizuma
ACES, Inc, Japan
Zhang, Jie
China Univ. of Chinese Academy of Sciences
He, Mingjie
China Univ. of Chinese Academy of Sciences
Han, Dan
China Univ. of Chinese Academy of Sciences
Shan, Shiguang
China Univ. of Chinese Academy of Sciences
Grm, Klemen
Univ. of Ljubljana
Struc, Vitomir
Univ. of Ljubljana
Seneviratne, Sachith
Univ. of Melbourne
Kasthuriarachchi, Nuran
Univ. of Moratuwa, Sri Lanka
Rasnayaka, Sanka
National Univ. of Singapore
Neto, Pedro C.
INESC TEC / Univ. of Porto, Portugal
Sequeira, Ana F.
Univ. of Porto, Portugal
Pinto, Joao Ribeiro
INESC TEC / Univ. of Porto, Portugal
Saffari, Mohsen
INESC TEC / Univ. of Porto, Portugal
Cardoso, Jaime S.
INESC TEC / Univ. of Porto, Portugal
Hauptwerk
IEEE International Joint Conference on Biometrics, IJCB 2021
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Joint Conference on Biometrics (IJCB) 2021
Thumbnail Image
DOI
10.1109/IJCB52358.2021.9484337
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic: Digitized Work

  • Lead Topic: Smart City

  • Research Line: Computer vision (CV)

  • Research Line: Machine Learning (ML)

  • biometrics

  • deep learning

  • machine learning

  • face recognition

  • Artificial Neural Networks

  • ATHENE

  • CRISP

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