Abstract:
Facial recognition algorithms power various
applications, demanding representative and diverse datasets.
However, developing reliable models for African populations is
hindered by the scarcity of African facial image databases. This
study addresses this gap by analyzing the state and potential of
African facial image collections. The methodology involves
collecting and analyzing indigenous African datasets and
evaluating factors like temporal relevance, geographic coverage,
and demographic representation. We evaluate the quality and
diversity of existing datasets, and the ethical and cultural issues of
data collection. We also apply machine-learning techniques,
namely Principal Component Analysis (PCA) and Support Vector
Machines (SVM), to analyze and classify facial features of three
African ethnic groups. The study shows that PCA can capture
facial variations, and SVM can achieve 55% accuracy, with group
differences. Findings highlight the potential of machine learning
for inclusive facial recognition but also reveal challenges,
including data imbalance and limitations in chosen features. To
achieve fair and reliable facial recognition, future directions
advocate for a culturally sensitive approach and highlight the
importance of representative dataset systems found in Africa.
Also, a concentration should be on collecting data from
underrepresented regions and ethnic groups. The collection of
diverse and culturally sensitive datasets can be facilitated by
collaborative activities between researchers and local
communities.