Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces
Fakunle Ajewole, Joseph Damilola Akinyemi, Khadijat Tope Ladoja,, Olufade Falade Williams Onifade

TL;DR
This study develops an age-invariant face recognition system specifically for indigenous African faces using deep learning, highlighting the importance of ethnicity-specific datasets for accurate recognition.
Contribution
The paper introduces a new African face dataset and demonstrates improved recognition accuracy for indigenous Africans using a pre-trained deep learning model.
Findings
Achieved 81.80% accuracy on indigenous African faces
Achieved 91.5% accuracy on African-American faces
Significant accuracy gap between indigenous and non-indigenous African recognition
Abstract
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…
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Taxonomy
TopicsFace recognition and analysis
