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In automatic face recognition the quality of the input image directly affects the accuracy of face identification and verification. In this work the authors presented a novel method for non-reference biometric evaluation of face image quality based on the analysis of cosine similarity between feature vectors extracted by a neural network recognition model. Unlike general image quality assessment methods like BRISQUE, the proposed approach utilizes both the distance to the class center and the degree of proximity to the most similar external classes, which allows more accurate ranking of images according to their suitability for biometric tasks. By using the AUC metric at different FMR levels, the experiments results on five common datasets (LFW, CALFW, AgeDB-30, CFP-FP, CPLFW) have demonstrated the advantage of the proposed method over modern approaches (CR-FIQA, SDD-FIQA, TOPIQ, etc.), thus it a promising technique for integration into practical biometric systems.

Key words: biometry, face quality assessment, face recognition, image quality assessment, neural networks
Published in: INFORMATION-COMMUNICATION TECHNOLOGIES
Bibliography link: Bordiuzha V., Umnyashkin S. V. Assessment method for biometric quality of facial images. Izv. vuzov. Elektronika = Proc. Univ. Electronics. 2026;31(4):487–494. (In Russ.). DOI: 10.24151/1561-5405-2026-31-4-487-494.YIBZTC

Viktor Bordiuzha
National Research University of Electronic Technology, Russia, 124498, Moscow, Zelenograd, Shokin sq., 1

Sergey V. Umnyashkin
National Research University of Electronic Technology, Russia, 124498, Moscow, Zelenograd, Shokin sq., 1

1. Mittal A., Moorthy A. K., Bovik A. C. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 2012;21(12):4695–4708. https://doi.org/10.1109/TIP.2012.2214050

2. Mittal A., Soundararajan R., Bovik A. C. Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 2013;20(3):209–212. https://doi.org/10.1109/LSP.2012.2227726

3. Бордюжа В., Умняшкин С. В. Ансамблирование метрик качества изображений для повышения корреляции с экспертными оценками. In: Цифровая обработка сигналов и ее применение DSPA–2024: докл. XXVI Междунар. конф. (Москва, 27–29 марта 2024 г.). М.: Рос. науч.-техн. о-во радиотехники, электроники и связи им. А. С. Попова; 2024, с. 187–192. EDN: BTDCNT.

Bordiuzha V., Umnyashkin S. V. Ensembling image quality metrics to enhance correlation with expert evaluations. In: Tsifrovaya obrabotka signalov i yeye primeneniye DSPA–2024: proc. of 26th International conf. (Moscow, March 27–29, 2024). Moscow: Ros. nauch.-tekhn. o-vo radiotekhniki, elektroniki i svyazi im. A. S. Popova Publ.; 2024, pp. 187–192. (In Russ).

4. Бордюжа В., Умняшкин С. В. Ансамблирование безэталонных мер качества изображений для повышения корреляции с экспертными оценками. Изв. вузов. Электроника. 2025;30(2):229–239. https://doi.org/10.24151/1561-5405-2025-30-2-229-239. EDN: EBIJVB.

Bordiuzha V., Umnyashkin S. V. The ensemble of reference-free image quality measures to improve correlation with expert evaluations. Izv. vuzov. Elektronika = Proc. Univ. Electronics. 2025;30(2):229–239. https://doi.org/10.24151/1561-5405-2025-30-2-229-239

5. Yi D., Lei Z., Liao S., Li S. Z. Learning face representation from scratch. arXiv.org. 28.11.2014. Available at: https://arxiv.org/pdf/1411.7923 (accessed: 26.06.2025).

6. Boutros F., Fang M., Klemt M., Fu B., Damer N. CR-FIQA: Face image quality assessment by learning sample relative classifiability. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE; 2023, pp. 5836–5845. https://doi.org/10.1109/CVPR52729.2023.00565

7. Ou F.-Z., Chen X., Zhang R., Huang Y., Li S., Li J. et al. SDD-FIQA: Unsupervised face image quality assessment with similarity distribution distance. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN: IEEE; 2021, pp. 7666–7675. https://doi.org/10.1109/CVPR46437.2021.00758

8. He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE; 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90

9. Deng J., Guo J., Xue N., Zafeiriou S. ArcFace: Additive angular margin loss for deep face recognition. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA: IEEE; 2019, pp. 4685–4694. https://doi.org/10.1109/CVPR.2019.00482

10. Deng J., Guo J., Ververas E., Kotsia I., Zafeiriou S. RetinaFace: Single-shot multi-level face localisation in the wild. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA: IEEE; 2020, pp. 5202–5211. https://doi.org/10.1109/CVPR42600.2020.00525

11. Huang G. B., Mattar M., Berg T., Learned-Miller E. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (Marseille, France, Oct. 2008). 16.09.2008. Available at: https://inria.hal.science/inria-00321923v1 (accessed: 04.05.2026).

12. Zheng T., Deng W., Hu J. Cross-Age LFW: A database for studying cross-age face recognition in unconstrained environments. arXiv.org. 2017. Available at: https://arxiv.org/abs/1708.08197 (accessed: 16.06.2025). https://doi.org/10.48550/arXiv.1708.08197

13. Moschoglou S., Papaioannou A., Sagonas C., Deng J., Kotsia I., Zafeiriou S. AgeDB: The first manually collected, in-the-wild age database. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, HI: IEEE; 2017, pp. 1997–2005. https://doi.org/10.1109/CVPRW.2017.250

14. Sengupta S., Chen J.-C., Castillo C., Patel V. M., Chellappa R., Jacobs D. W. Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid, NY: IEEE; 2016, pp. 1–9. https://doi.org/10.1109/WACV.2016.7477558

15. Zheng T., Deng W. Cross-Pose LFW: A database for studying cross-pose face recognition in unconstrained environments. Weihong Deng. 2018. Available at: http://www.whdeng.cn/CPLFW/Cross-Pose-LFW.pdf (accessed: 06.05.2026).

16. Chen C., Mo J., Hou J., Wu H., Liao L., Sun W. et al. TOPIQ: A top-down approach from semantics to distortions for image quality assessment. arXiv.org. 2023. Available at: https://arxiv.org/abs/2308.03060 (accessed: 15.06.2024). https://doi.org/10.48550/arXiv.2308.03060

17. Hernandez-Ortega J., Galbally J., Fierrez J., Haraksim R., Beslay L. FaceQnet: Quality assessment for face recognition based on deep learning. In: 2019 International Conference on Biometrics (ICB). Crete: IEEE; 2019, pp. 1–8. https://doi.org/10.1109/ICB45273.2019.8987255

18. Huang Y., Wang Y., Tai Y., Liu X., Shen P., Li S. CurricularFace: Adaptive curriculum learning loss for deep face recognition. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA: IEEE; 2020, pp. 5900–5909. https://doi.org/10.1109/CVPR42600.2020.00594

19. Meng Q., Zhao S., Huang Z., Zhou F. MagFace: A universal representation for face recognition and quality assessment. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN: IEEE; 2021, pp. 14220–14229. https://doi.org/10.1109/CVPR46437.2021.01400

20. Grother P., Tabassi E. Performance of biometric quality measures. IEEE Trans. Pattern Anal. Mach. Intell. 2007;29(4):531–543. https://doi.org/10.1109/TPAMI.2007.1019

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UDK: 004.932.4
Publication type: Scientific article
Source lang: Russian
DOI: 10.24151/1561-5405-2026-31-4-487-494
RISC id: YIBZTC

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