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arXiv · 2201.00461

Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%

Leonardo Queiroz·Kenneth Lai·Svetlana Yanushkevich·Vlad Shmerko
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Abstract

In this study of the face recognition on masked versus unmasked faces generated using Flickr-Faces-HQ and SpeakingFaces datasets, we report 36.78% degradation of recognition performance caused by the mask-wearing at the time of pandemics, in particular, in border checkpoint scenarios. We have achieved better performance and reduced the degradation to 1.79% using advanced deep learning approaches in the cross-spectral domain.

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Leonardo Queiroz, Kenneth Lai, Svetlana Yanushkevich, Vlad Shmerko. 2022-01-03. Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%. https://arxiv.org/abs/2201.00461

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