FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

• URL: http://arxiv.org/abs/2110.14940v1
• Date: Thu, 28 Oct 2021 08:17:12 GMT
• Title: FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
• Authors: Pedro C. Neto, Fadi Boutros, Jo\~ao Ribeiro Pinto, Naser Damer, Ana F. Sequeira and Jaime S. Cardoso
• Abstract summary: SARS-CoV-2 has presented direct and indirect challenges to the scientific community. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition.
• Score: 4.420321822469077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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