2 research outputs found
Comparative analysis of features extraction techniques for black face age estimation
A computer-based age estimation is a technique that predicts an individual's
age based on visual traits derived by analyzing a 2D picture of the individual's
face. Age estimation is critical for access control, e-government, and effective
human–computer interaction. The other-race effect has the potential to cause
techniques designed for white faces to underperform when used in a region
with black faces. The outcome is the consequence of intermittent training with
faces of the same race and the encoding structure of the trained face images,
which is based on the feature extraction technique used. This study
contributes to a constructive comparison of three feature-extraction
techniques, namely, local binary pattern (LBP), Gabor Wavelet (GW), and
wavelet transformation, used in the development of a genetic algorithm (GA)-
artificial neural network (ANN)-based age estimation system. The feature
extraction techniques used are proven to produce a wealth of shape and
textural information. The GA-ANN constitutes the age classifier module. The
correct classification rate was chosen as the performance metrics in this study.
The results demonstrated that the LBP is a more robust representation of the
black face than the GW and Wavelet transformations, as evidenced by its accuracy rate of 91.76 compared to 89.41 and 84.71 achieved with the GW and
Wavelet transformation age estimation systems, respectively