Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces
<|reference_start|>Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces: 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.<|reference_end|>
@article{ajewole2024unmasking,
    title={Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition
  of Indigenous African Faces},
    author={Fakunle Ajewole, Joseph Damilola Akinyemi, Khadijat Tope Ladoja,
  Olufade Falade Williams Onifade},
    journal={arXiv preprint arXiv:2408.06806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06806},
    primaryClass={cs.CV cs.AI}
}