2024/08/13 by Fakunle Ajewole, Joseph Damilola Akinyemi, Ajewole, Fakunle +5
Computer Science · #Face recognition and analysis
paper · pdf · doi:10.48550/arxiv.2408.06806
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.