An Evaluation of Features Extracted from Facial Images in the Context of Accurate Age Estimation

Age estimation by face image recognition can be used in numerous ways with regression models to manage access control, improve security, and guarantee the protection of children online. The approaches used for predicting age—including data selection, cleaning techniques, feature extraction, algorithm choice, and hyperparameter tuning—often struggles with generalization. Furthermore, a lot of methods neglect to specifically address how extracted face features might be used for prediction. To address the lack of racial diversity we acquired a dataset consisting of different races from literature. We also examined the ability of local, global and hybrid facial features to predict ages. Two variants of Local Binary Pattern (LBP) were used to extract local features: one variant based on the number of uniform patterns produced 16,384 features, and another variant based on a fixed-length histogram bins produced 10 features. We have used 12, 25, 35 and 37 face ratios and Euclidean distances between different facial landmarks for global feature extraction. All the feature sets are evaluated using Pearson correlation, F-regression, and Information Gain to assess the predictive capability of the features. Finally, the random forest regressor is applied on the extracted features via different models to evaluate the Mean Absolute Error (MAE) and R-square (R2). The results indicate that the 37 geometric facial ratios and Euclidean distances outperform all other models achieving the lowest MAE of 1.99 years and an R2 of 0.90. Our results show that geometrical features are more effective in the context of age regression, yielding fewer features that are more relevant to accurate age estimation. This minimizes the need for feature selection and reduction techniques, which we assume can lead to increased computational costs.