Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces
This addresses the misrepresentation of African ethnicity in facial analysis research, which is an incremental improvement by applying existing methods to a new, underrepresented dataset.
The paper tackled the under-representation of indigenous African faces in age-invariant face recognition (AIFR) by developing a system using a pre-trained VGGFace model on a new dataset of 5,000 indigenous African faces, achieving 81.80% accuracy, and found a significant accuracy difference compared to non-indigenous Africans, with 91.5% on an African-American subset.
The task of recognizing the age-separated faces of an individual, Age-Invariant Face Recognition (AIFR), has received considerable research efforts in Europe, America, and Asia, compared to Africa. Thus, AIFR research efforts have often under-represented/misrepresented the African ethnicity with non-indigenous Africans. This work developed an AIFR system for indigenous African faces to reduce the misrepresentation of African ethnicity in facial image analysis research. We adopted a pre-trained deep learning model (VGGFace) for AIFR on a dataset of 5,000 indigenous African faces (FAGE\_v2) collected for this study. FAGE\_v2 was curated via Internet image searches of 500 individuals evenly distributed across 10 African countries. VGGFace was trained on FAGE\_v2 to obtain the best accuracy of 81.80\%. We also performed experiments on an African-American subset of the CACD dataset and obtained the best accuracy of 91.5\%. The results show a significant difference in the recognition accuracies of indigenous versus non-indigenous Africans.