Authors :
Segun Aina; Mosunmola Oluwabusola Adeniji; Aderonke Rasheedat Lawal; Adeniran Isola Oluwaranti
Volume/Issue :
Volume 7 - 2022, Issue 4 - April
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3LB8UPj
DOI :
https://doi.org/10.5281/zenodo.6568417
Abstract :
- The human face is considered to be the seat of
man’s identity and information such as age and ethnicity
are often automatically deduced from the face by people.
However, deducing the same information by a
computing system is not a straight forward process and
have in recent years be powered by Convolutional
Neural Networks (CNN). CNN can automatically
extract hidden patterns in data. These hidden patterns
are often complex to represent using hand-crafted
representation methods. Although automated
classification of demographic traits such as age, gender
and ethnicity is a well-studied research problem, it is still
far from being considered a solved problem for Nigerian
ethnic groups. In this paper, a CNN model for ethnicity
classification of Nigerians from facial images is proposed
based on transfer learning techniques conducted on
VGG-16 architecture. The model is evaluated on a
dataset consisting of facial images of Yoruba, Hausa and
Igbo ethnic groups of Nigeria. The developed VGG-16
based ethnicity classification model had an overall
accuracy of 92.86%, with the precision, sensitivity and
specificity shedding more light on the model’s
performance.
- The human face is considered to be the seat of
man’s identity and information such as age and ethnicity
are often automatically deduced from the face by people.
However, deducing the same information by a
computing system is not a straight forward process and
have in recent years be powered by Convolutional
Neural Networks (CNN). CNN can automatically
extract hidden patterns in data. These hidden patterns
are often complex to represent using hand-crafted
representation methods. Although automated
classification of demographic traits such as age, gender
and ethnicity is a well-studied research problem, it is still
far from being considered a solved problem for Nigerian
ethnic groups. In this paper, a CNN model for ethnicity
classification of Nigerians from facial images is proposed
based on transfer learning techniques conducted on
VGG-16 architecture. The model is evaluated on a
dataset consisting of facial images of Yoruba, Hausa and
Igbo ethnic groups of Nigeria. The developed VGG-16
based ethnicity classification model had an overall
accuracy of 92.86%, with the precision, sensitivity and
specificity shedding more light on the model’s
performance.
