Two-stage dual-resolution face network for cross-resolution face recognition in surveillance systems
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Fujian Province
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03121-4.pdf
Reference38 articles.
1. Zangeneh, E., Rahmati, M., Mohsenzadeh, Y.: Low resolution face recognition using a two-branch deep convolutional neural network architecture. Expert Syst. Appl. 139, 112854 (2020). https://doi.org/10.1016/j.eswa.2019.112854
2. Ze, L., Jiang, X., Kot, A.: Deep coupled resnet for low-resolution face recognition. IEEE Signal Process. Lett. 25(4), 526–530 (2018). https://doi.org/10.1109/lsp.2018.2810121
3. Zha J., Chao, H.: Tcn: transferable coupled network for cross-resolution face recognition. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3302–3306. IEEE (2019). https://doi.org/10.1109/icassp.2019.8682384
4. Massoli, F.V., Amato, G., Falchi, F.: Cross-resolution learning for face recognition. Image Vis. Comput. 99, 103927 (2020). https://doi.org/10.1016/j.imavis.2020.103927
5. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015). https://doi.org/10.1109/cvpr.2015.7298682
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