Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2092-7_3
Reference54 articles.
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3. Babnik, Ž., Peer, P., Štruc, V.: FaceQAN: face image quality assessment through adversarial noise exploration. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 748–754. IEEE (2022)
4. Best-Rowden, L., Jain, A.K.: Learning face image quality from human assessments. IEEE Trans. Inf. Forensics Secur. 13(12), 3064–3077 (2018)
5. Boutros, F., Fang, M., Klemt, M., Fu, B., Damer, N.: CR-FIQA: face image quality assessment by learning sample relative classifiability. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5836–5845 (2023)
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