CLIB-FIQA : Face Image Quality Assessment with Confidence Calibration

Fu Zhao OU, Chongyi LI, Shiqi WANG, Sam KWONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

Face Image Quality Assessment (FIQA) is pivotal for guaranteeing the accuracy of face recognition in unconstrained environments. Recent progress in deep quality-fitting-based methods that train models to align with quality anchors, has shown promise in FIQA. However, these methods heavily depend on a recognition model to yield quality anchors and indiscriminately treat the confidence of inaccurate anchors as equivalent to that of accurate ones during the FIQA model training, leading to a fitting bottleneck issue. This paper seeks a solution by putting forward the Confidence-Calibrated Face Image Quality Assessment (CLIB-FIQA) approach, underpinned by the synergistic interplay between the quality anchors and objective quality factors such as blur, pose, expression, occlusion, and illumination. Specifically, we devise a joint learning framework built upon the vision-language alignment model, which leverages the joint distribution with multiple quality factors to facilitate the quality fitting of the FIQA model. Furthermore, to alleviate the issue of the model placing excessive trust in inaccurate quality anchors, we propose a confidence calibration method to correct the quality distribution by exploiting to the fullest extent of these objective quality factors characterized as the merged-factor distribution during training. Experimental results on eight datasets reveal the superior performance of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
PublisherIEEE Computer Society
Pages1694-1704
Number of pages11
ISBN (Electronic)9798350353006
DOIs
Publication statusE-pub ahead of print - 16 Sept 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States
Duration: 16 Jun 202422 Jun 2024

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
Country/TerritoryUnited States
CitySeattle
Period16/06/2422/06/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This work is partially supported by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), the Research Grant Council (RGC) of Hong Kong General Research Fund (GRF) under Grant 11209819, Grant 11203820, and Grant 11203220, the CityU Applied Research Grant 9667255, and in part by the Strategic Interdisciplinary Research Grant Project 7020055.

Fingerprint

Dive into the research topics of 'CLIB-FIQA : Face Image Quality Assessment with Confidence Calibration'. Together they form a unique fingerprint.

Cite this