Boosting Masked Face Recognition with Multi-Task ArcFace

• URL: http://arxiv.org/abs/2104.09874v2
• Date: Wed, 21 Apr 2021 06:54:29 GMT
• Title: Boosting Masked Face Recognition with Multi-Task ArcFace
• Authors: David Montero, Marcos Nieto, Peter Leskovsky and Naiara Aginako
• Abstract summary: Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. A full training pipeline is presented based on the ArcFace work, with several modifications for the backbone and the loss function.
• Score: 0.973681576519524
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we address the problem of face recognition with masks. Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This sanitary measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. In addition, the need has arisen for applications capable of detecting whether the subjects are wearing masks to control the spread of the virus. To overcome these problems a full training pipeline is presented based on the ArcFace work, with several modifications for the backbone and the loss function. From the original face-recognition dataset, a masked version is generated using data augmentation, and both datasets are combined during the training process. The selected network, based on ResNet-50, is modified to also output the probability of mask usage without adding any computational cost. Furthermore, the ArcFace loss is combined with the mask-usage classification loss, resulting in a new function named Multi-Task ArcFace (MTArcFace). Experimental results show that the proposed approach highly boosts the original model accuracy when dealing with masked faces, while preserving almost the same accuracy on the original non-masked datasets. Furthermore, it achieves an average accuracy of 99.78% in mask-usage classification.

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