Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 49 references that are in the map. First posted on arXiv 3 Jan 2022.
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References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
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- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
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- 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
- 2017SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verificationcited by 31
- 2020MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19cited by 31
- 2020SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streamscited by 21
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