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.

Feature Factorization -- A linear factorization module is introduced that decomposes the entire set of facial features into two sets of uncorrelated components(age & identity). This is followed by retrieving age-related details through a mapping function'R' and the residual part is considered as the identity component. During the inference time, only the identity-related features are utilised for face recognition. The first backbone network is similar to ResNets which extracts the initial features from the entire image. Decorrelated Adversarial Learning -- Even though we want both the components to be independent of each other, practically identity has some mix of features from the age information.