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My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition

Neto, Pedro C. ; Boutros, Fadi ; Pinto, Joao Ribeiro ; Saffari, Mohsen ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S. (2021)
My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition.
20th International Conference of the Biometrics Special Interest Group. virtual Conference (15.-17.09.2021)
doi: 10.1109/BIOSIG52210.2021.9548320
Conference or Workshop Item, Bibliographie

Abstract

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose 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. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Neto, Pedro C. ; Boutros, Fadi ; Pinto, Joao Ribeiro ; Saffari, Mohsen ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S.
Type of entry: Bibliographie
Title: My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition
Language: English
Date: 27 September 2021
Publisher: IEEE
Book Title: BIOSIG 2021: Proceedings of the 20th International Conference of the Biometrics Special Interest Group
Event Title: 20th International Conference of the Biometrics Special Interest Group
Event Location: virtual Conference
Event Dates: 15.-17.09.2021
DOI: 10.1109/BIOSIG52210.2021.9548320
Abstract:

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose 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. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.

Uncontrolled Keywords: Biometrics, Face recognition, Deep learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 29 Sep 2021 13:12
Last Modified: 29 Sep 2021 13:12
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