Real-time masked face recognition using deep learning-based double generator network
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03155-8.pdf
Reference25 articles.
1. Zhang, Y., Wang, X., Shakeel, M.S., Wan, H., Kang, W.: Learning upper patch attention using dual-branch training strategy for masked face recognition. Pattern Recognit. 126, 108522 (2022). https://doi.org/10.1016/j.patcog.2022.108522
2. Jeevan, G., Zacharias, G.C., Nair, M.S., Rajan, J.: An empirical study of the impact of masks on face recognition. Pattern Recognit. 122, 108308 (2022). https://doi.org/10.1016/j.patcog.2021.108308
3. Ullah, N., Javed, A., Ghazanfar, M.A., Alsufyani, A., Bourouis, S.: A novel DeepMaskNet model for face mask detection and masked facial recognition. J. King Saud Univ. Comput. Inf. Sci. (2022). https://doi.org/10.1016/j.jksuci.2021.12.017
4. Wang, J., Li, S., Luo, F.: Cross-age face recognition using deep learning model based on dual attention mechanism. In Communications, Signal Processing, and Systems: Proceedings of the 9th International Conference on Communications, J. Signal Process. Syst. pp. 1911–1919. Singapore: Springer Singapore. (2021). https://doi.org/10.1007/978-981-15-8411-4_251
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