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 (15.09.2021-17.09.2021)
doi: 10.1109/BIOSIG52210.2021.9548320
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Neto, Pedro C. ; Boutros, Fadi ; Pinto, Joao Ribeiro ; Saffari, Mohsen ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S. |
| Art des Eintrags: | Bibliographie |
| Titel: | My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition |
| Sprache: | Englisch |
| Publikationsjahr: | 27 September 2021 |
| Verlag: | IEEE |
| Buchtitel: | BIOSIG 2021: Proceedings of the 20th International Conference of the Biometrics Special Interest Group |
| Veranstaltungstitel: | 20th International Conference of the Biometrics Special Interest Group |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 15.09.2021-17.09.2021 |
| DOI: | 10.1109/BIOSIG52210.2021.9548320 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Face recognition, Deep learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 29 Sep 2021 13:12 |
| Letzte Änderung: | 29 Sep 2021 13:12 |
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