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, Ugur ; Ekenel, Hazim 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. (2021)
MFR 2021: Masked Face Recognition Competition.
2021 IEEE International Joint Conference on Biometrics (IJCB). virtual Conference (04.-07.08.2021)
doi: 10.1109/IJCB52358.2021.9484337
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | 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, Ugur ; Ekenel, Hazim 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. |
| Art des Eintrags: | Bibliographie |
| Titel: | MFR 2021: Masked Face Recognition Competition |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 2021 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 04.-07.08.2021 |
| DOI: | 10.1109/IJCB52358.2021.9484337 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Deep learning, Machine learning, Face recognition, Artificial neural networks |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 03 Aug 2021 13:11 |
| Letzte Änderung: | 03 Aug 2021 13:11 |
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