  Iris biometrics is a phenotypic biometric trait that has proven to be
agnostic to human natural physiological changes. Research on iris biometrics
has progressed tremendously, partly due to publicly available iris databases.
Various databases have been available to researchers that address pressing iris
biometric challenges such as constraint, mobile, multispectral, synthetics,
long-distance, contact lenses, liveness detection, etc. However, these
databases mostly contain subjects of Caucasian and Asian docents with very few
Africans. Despite many investigative studies on racial bias in face biometrics,
very few studies on iris biometrics have been published, mainly due to the lack
of racially diverse large-scale databases containing sufficient iris samples of
Africans in the public domain. Furthermore, most of these databases contain a
relatively small number of subjects and labelled images. This paper proposes a
large-scale African database named CASIA-Iris-Africa that can be used as a
complementary database for the iris recognition community to mediate the effect
of racial biases on Africans. The database contains 28,717 images of 1023
African subjects (2046 iris classes) with age, gender, and ethnicity attributes
that can be useful in demographically sensitive studies of Africans. Sets of
specific application protocols are incorporated with the database to ensure the
database's variability and scalability. Performance results of some open-source
SOTA algorithms on the database are presented, which will serve as baseline
performances. The relatively poor performances of the baseline algorithms on
the proposed database despite better performance on other databases prove that
racial biases exist in these iris recognition algorithms. The database will be
made available on our website: http://www.idealtest.org.
