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FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

Neto, Pedro C. ; Boutros, Fadi ; Pinto, Joao Ribeiro ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S. (2021)
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition.
16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). virtual Conference (15.-18.12.2021)
doi: 10.1109/FG52635.2021.9666792
Conference or Workshop Item, Bibliographie

Abstract

SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (M-M) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared to. We further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Neto, Pedro C. ; Boutros, Fadi ; Pinto, Joao Ribeiro ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S.
Type of entry: Bibliographie
Title: FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
Language: English
Date: 2021
Publisher: IEEE
Book Title: Proceedings: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
Event Title: 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
Event Location: virtual Conference
Event Dates: 15.-18.12.2021
DOI: 10.1109/FG52635.2021.9666792
Abstract:

SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (M-M) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared to. We further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub.

Uncontrolled Keywords: Face recognition, Machine learning, Deep learning, Biometrics
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 03 Mar 2022 08:51
Last Modified: 19 May 2022 06:15
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