Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision
Subtitle of host publicationICCV 2023
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages20661-20672
ISBN (electronic)9798350307184
ISBN (print)979-8-3503-0719-1
Publication statusPublished - Oct 2023

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499
ISSN (electronic)2380-7504

Conference

TitleIEEE International Conference on Computer Vision 2023 (ICCV 2023)
LocationParis Convention Center
PlaceFrance
CityParis
Period2 - 6 October 2023

Abstract

Face Image Quality Assessment (FIQA) lays the foundation for ensuring the stability and accuracy of face recognition systems. However, existing FIQA methods mainly formulate quality relationships within the training set to yield quality scores, ignoring the generalization problem caused by ethnic quality bias between the training and test sets. Domain adaptation presents a potential solution to mitigate the bias, but if FIQA is treated essentially as a regression task, it will be limited by the challenge of feature scaling in transfer learning. Additionally, how to guarantee source risk is also an issue due to the lack of ground-truth labels of the source domain for FIQA. This paper presents the first attempt in the field of FIQA to address these challenges with a novel Ethnic-Quality-Bias Mitigating (EQBM) framework. Specifically, to eliminate the restriction of scalar regression, we first compute the Likert-scale quality probability distributions as source domain annotations. Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation. Extensive experiments demonstrate that the EQBM significantly mitigates the quality bias and improves the generalization capability of FIQA across races on different datasets. © 2023 IEEE.

Citation Format(s)

Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment. / Ou, Fu-Zhao; Chen, Baoliang; Li, Chongyi et al.
Proceedings - 2023 IEEE/CVF International Conference on Computer Vision: ICCV 2023. Institute of Electrical and Electronics Engineers, Inc., 2023. p. 20661-20672 (Proceedings of the IEEE International Conference on Computer Vision).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review