MFR 2021: Masked Face Recognition Competition
- URL: http://arxiv.org/abs/2106.15288v1
- Date: Tue, 29 Jun 2021 11:59:56 GMT
- Title: MFR 2021: Masked Face Recognition Competition
- Authors: Fadi Boutros, Naser Damer, Jan Niklas Kolf, Kiran Raja, Florian
Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper, Pengcheng Fang, Chao
Zhang, Fei Wang, David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto,
Mustafa Ekrem Erakin, Ugur Demir, Hazim Kemal, Ekenel, Asaki Kataoka, Kohei
Ichikawa, Shizuma Kubo, Jie Zhang, Mingjie He, Dan Han, Shiguang Shan, Klemen
Grm, Vitomir \v{S}truc, Sachith Seneviratne, Nuran Kasthuriarachchi, Sanka
Rasnayaka, Pedro C. Neto, Ana F. Sequeira, Joao Ribeiro Pinto, Mohsen
Saffari, and Jaime S. Cardoso
- Abstract summary: 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.
The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces.
- Score: 43.60381669339876
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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, multi-session, real masked, capture
scenario is used to evaluate the submitted solutions. In comparison to one of
the top-performing academic face recognition solutions, 10 out of the 18
submitted solutions did score higher masked face verification accuracy.
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