Measuring Hidden Bias within Face Recognition via Racial Phenotypes
2021
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.
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