Boutros, Fadi ; Fang, Meiling ; Klemt, Marcel ; Fu, Biying ; Damer, Naser (2023)
CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada (18.-22.06.2023)
doi: 10.1109/CVPR52729.2023.00565
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 1 https://github.com/fdbtrs/CR-FIQA
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Boutros, Fadi ; Fang, Meiling ; Klemt, Marcel ; Fu, Biying ; Damer, Naser |
| Art des Eintrags: | Bibliographie |
| Titel: | CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability |
| Sprache: | Englisch |
| Publikationsjahr: | 22 August 2023 |
| Verlag: | IEEE |
| Buchtitel: | Proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
| Veranstaltungstitel: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023) |
| Veranstaltungsort: | Vancouver, Canada |
| Veranstaltungsdatum: | 18.-22.06.2023 |
| DOI: | 10.1109/CVPR52729.2023.00565 |
| Kurzbeschreibung (Abstract): | Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 1 https://github.com/fdbtrs/CR-FIQA |
| Freie Schlagworte: | Face recognition, Biometrics, Deep learning, Machine learning, Quality estimation |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 04 Dez 2023 12:51 |
| Letzte Änderung: | 31 Jan 2024 07:52 |
| PPN: | 515148504 |
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