  Facial recognition is one of the most academically studied and industrially
developed areas within computer vision where we readily find associated
applications deployed globally. This widespread adoption has uncovered
significant performance variation across subjects of different racial profiles
leading to focused research attention on racial bias within face recognition
spanning both current causation and future potential solutions. In support,
this study provides an extensive taxonomic review of research on racial bias
within face recognition exploring every aspect and stage of the face
recognition processing pipeline. Firstly, we discuss the problem definition of
racial bias, starting with race definition, grouping strategies, and the
societal implications of using race or race-related groupings. Secondly, we
divide the common face recognition processing pipeline into four stages: image
acquisition, face localisation, face representation, face verification and
identification, and review the relevant corresponding literature associated
with each stage. The overall aim is to provide comprehensive coverage of the
racial bias problem with respect to each and every stage of the face
recognition processing pipeline whilst also highlighting the potential pitfalls
and limitations of contemporary mitigation strategies that need to be
considered within future research endeavours or commercial applications alike.
