# Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces

arXiv: https://arxiv.org/abs/2408.06806 · HTML: https://mlsanity.com/paper/2408.06806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition](https://mlsanity.com/paper/1810.07599/) (2018)
- [Unconstrained Face Verification using Deep CNN Features](https://mlsanity.com/paper/1508.01722/) (2015)
- [Large age-gap face verification by feature injection in deep networks](https://mlsanity.com/paper/1602.06149/) (2016)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark](https://mlsanity.com/paper/2210.09835/) (2022)

## Abstract (arXiv)

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 (FAGEv2) collected for this study. FAGEv2 was curated via Internet image searches of 500 individuals evenly distributed across 10 African countries. VGGFace was trained on FAGEv2 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.