Enhancement of Accuracy in Facial Age Identification using Comparing the Ensemble Support Vector Machine Algorithm to the Linear Regression Algorithm

In comparison to the Linear Regression (LR) approach, the Innovative Ensemble Support Vector Machine (ESVM) method's predictive power for face age prediction is to be assessed in this study. For the purpose of identifying age in a facial images and the dataset were sourced from Kaggle, comprising 20 facial samples in each of Group 1 and Group 2. The comparison between the ESVM and Linear Regression algorithms was conducted using a G Power calculator, with a pre-test power of 80% and an alpha value set at 0.05. When compared to the Ridge regression technique has an accuracy of 81.51 % and the Innovative Ensemble Support Vector Machine (ESVM) method achieved an accuracy of 86.50% with a significant independent sample t-test result of 0.001 (p < 0.05). The results shows the ESVM method has demonstrated a statistically significant improvement over the Linear Regression method for analyzing and identifying facial age, with a 86.50 % accuracy rate.