EfficientMask-Net for face authentication in the era of COVID-19 pandemic
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-022-02160-z.pdf
Reference21 articles.
1. Azouji, N., Sami, A., Taheri, M., Müller, H.: A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19. Comput. Biol. Med. 139, 104927 (2021)
2. Prasad, S., Li, Y., Lin, D., Sheng, D.: maskedFaceNet: a progressive semi-supervised masked face detector. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3389–3398 (2021)
3. Fasfous, N., Vemparala, M.-R., Frickenstein, A., Frickenstein, L., Stechele, W.: BinaryCoP: Binary neural network-based COVID-19 face-mask wear and positioning predictor on edge devices. arXiv Prepr. arXiv2102.03456 (2021)
4. Cabani, A., Hammoudi, K., Benhabiles, H., Melkemi, M.: MaskedFace-net–a dataset of correctly/incorrectly masked face images in the context of COVID-19. Smart Heal. 19, 100144 (2021)
5. Qin, B., Li, D.: Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19. Sensors 20, 5236 (2020)
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