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MFR 2021: Masked Face Recognition Competition

 
: Boutros, Fadi; Damer, Naser; Kolf, Jan Niklas; Raja, Kiran; Kirchbuchner, Florian; Ramachandra, Raghavendra; Kuijper, Arjan; Fang, Pengcheng; Zhang, Chao; Wang, Fei; Montero, David; Aginako, Naiara; Sierra, Basilio; Nieto, Marcos; Erakin, Mustafa Ekrem; Demir, Uğur; Ekenel, Hazım Kemal; Kataoka, Asaki; Ichikawa, Kohei; Kubo, Shizuma; Zhang, Jie; He, Mingjie; Han, Dan; Shan, Shiguang; Grm, Klemen; Struc, Vitomir; Seneviratne, Sachith; Kasthuriarachchi, Nuran; Rasnayaka, Sanka; Neto, Pedro C.; Sequeira, Ana F.; Pinto, Joao Ribeiro; Saffari, Mohsen; Cardoso, Jaime S.

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Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Biometrics Council; International Association for Pattern Recognition -IAPR-:
IEEE International Joint Conference on Biometrics, IJCB 2021 : 4-7 August 2021, Shenzhen, China, virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-3781-3
ISBN: 978-1-6654-3780-6
Art. 9484337, 10 pp.
International Joint Conference on Biometrics (IJCB) <2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
ATHENE
English
Conference Paper
Fraunhofer IGD ()
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

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.

: http://publica.fraunhofer.de/documents/N-638536.html