Abstract
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.
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VCO and EOO conceived the idea of the work. VCO and EOO designed the work OO, and VCO participated in the experiment execution. VCO, OO and EOO participated in writing of the manuscript. VCO and EOO supervised the work. All the authors reviewed the manuscript and approved it for submission.
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Oladipo, O., Omidiora, E.O. & Osamor, V.C. Comparative analysis of features extraction techniques for black face age estimation. AI & Soc (2022). https://doi.org/10.1007/s00146-022-01407-0
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DOI: https://doi.org/10.1007/s00146-022-01407-0