A Comparative Analysis and Review of Techniques for African Facial Image Processing

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.