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Measuring Hidden Bias within Face Recognition via Racial Phenotypes

Yucer, S. and Tekras, F. and Al Moubayed, N. and Breckon, T.P. (2022) 'Measuring Hidden Bias within Face Recognition via Racial Phenotypes.', Proc. Winter Conference on Applications of Computer Vision Waikoloa, HI, 4-8 Jan 2022.

Abstract

Recent work reports disparate performance for intersectional racial groups across face recognition tasks: face verification and identification. However, the definition of those racial groups has a significant impact on the underlying findings of such racial bias analysis. Previous studies define these groups based on either demographic information (e.g. African, Asian etc.) or skin tone (e.g. lighter or darker skins). The use of such sensitive or broad group definitions has disadvantages for bias investigation and subsequent counter-bias solutions design. By contrast, this study introduces an alternative racial bias analysis methodology via facial phenotype attributes for face recognition. We use the set of observable characteristics of an individual face where a race-related facial phenotype is hence specific to the human face and correlated to the racial profile of the subject. We propose categorical test cases to investigate the individual influence of those attributes on bias within face recognition tasks. We compare our phenotypebased grouping methodology with previous grouping strategies and show that phenotype-based groupings uncover hidden bias without reliance upon any potentially protected attributes or ill-defined grouping strategies. Furthermore, we contribute corresponding phenotype attribute category labels for two face recognition tasks: RFW for face verification and VGGFace2 (test set) for face identification.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://wacv2022.thecvf.com/home
Publisher statement:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:04 October 2021
Date deposited:19 October 2021
Date of first online publication:January 2022
Date first made open access:09 January 2022

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