Extended evaluation of the effect of real and simulated masks on face recognition performance.
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In this paper, the effect of mask-wearing on face recognition in a collaborative environment is investigated by using face images with synthetic mask-like face occlusions without exclusively assessing how representative they are of real face masks.Abstract:
Face recognition is an essential technology in our daily lives as a contactless and convenient method of accurate identity verification. Processes such as secure login to electronic devices or identity verification at automatic border control gates are increasingly dependent on such technologies. The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. The pandemic has led to the wide use of face masks, essential to keep the pandemic under control. The effect of mask-wearing on face recognition in a collaborative environment is currently a sensitive yet understudied issue. Recent reports have tackled this by using face images with synthetic mask-like face occlusions without exclusively assessing how representative they are of real face masks. These issues are addressed by presenting a specifically collected database containing three sessions, each with three different capture instructions, to simulate real use cases. The data are augmented to include previously used synthetic mask occlusions. Further studied is the effect of masked face probes on the behaviour of four face recognition systems-three academic and one commercial. This study evaluates both masked-to-non-masked and masked-to-masked face comparisons. In addition, real masks in the database are compared with simulated masks to determine their comparative effects on face recognition performance.read more
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Journal ArticleDOI
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TL;DR: Wang et al. as mentioned in this paper proposed the Embedding Unmasking Model (EUM) operated on top of existing face recognition models, which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities.
Proceedings ArticleDOI
MFR 2021: Masked Face Recognition Competition
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TL;DR: The Masked Face Recognition Competition (MFR) as discussed by the authors was held within the 2021 International Joint Conference on Biometrics (IJCB 2021) and attracted a total of 10 participating teams with valid submissions.
Journal ArticleDOI
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Proceedings ArticleDOI
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TL;DR: In this article, the authors proposed 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.
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