MFR 2021: Masked Face Recognition Competition
Fadi Boutros,Naser Damer,Jan Niklas Kolf,Kiran B. 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 Struc,Sachith Seneviratne,Nuran Kasthuriarachchi,Sanka Rasnayaka,Pedro C. Neto,Ana F. Sequeira,Joao Ribeiro Pinto,Mohsen Saffari,Jaime S. Cardoso +33 more
TLDR
The Masked Face Recognition Competition (MFR) as discussed by the authors was held within the 2021 International Joint Conference on Biometrics (IJCB 2021) and attracted a total of 10 participating teams with valid submissions.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.read more
Citations
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Journal ArticleDOI
A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19
TL;DR: Wang et al. as discussed by the authors presented a comprehensive survey of Masked Facial Detection using Artificial Intelligence (AI) techniques and their applications in real world scenarios such as safety monitoring, disease diagnosis, infection risk assessment, and lesion segmentation of COVID-19 CT scans.
Proceedings ArticleDOI
My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition
Pedro C. Neto,Fadi Boutros,Joao Ribeiro Pinto,Mohsen Saffari,Naser Damer,Ana F. Sequeira,Jaime S. Cardoso +6 more
TL;DR: In this article, the authors proposed a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode.
Journal ArticleDOI
Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey
Renu Sharma,Arun Ross +1 more
TL;DR: The performance of face recognition systems can be negatively impacted in the presence of masks and other types of facial coverings that have become prevalent due to the COVID-19 pandemic, so the periocular region of the human face becomes an important biometric cue.
Journal ArticleDOI
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models
TL;DR: A novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model, and demonstrates the superiority of this approach on intra- and cross-device periocular verification.
Journal ArticleDOI
Masked face recognition: Human versus machine
TL;DR: In this article , a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic face recognition solutions is presented. But, the authors do not consider the effect of wearing a mask on face recognition.
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