Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces
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arXiv
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| Format: | Preprint |
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2024
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| author | Ajewole, Fakunle Akinyemi, Joseph Damilola Ladoja, Khadijat Tope Onifade, Olufade Falade Williams |
| author_facet | Ajewole, Fakunle Akinyemi, Joseph Damilola Ladoja, Khadijat Tope Onifade, Olufade Falade Williams |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06806 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces Ajewole, Fakunle Akinyemi, Joseph Damilola Ladoja, Khadijat Tope Onifade, Olufade Falade Williams Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2408.06806 |