In 2014, Guo and Mu introduced a framework to simultaneously estimate age, gender, and race using canonical correlation analysis (CCA) based methods for dimension reduction, and multivariate regression techniques on a large face database of MORPH-II. This study expands their framework by considering additional facial feature extraction methods, as well as, Feature Fusion, a strategy that involves integrating multiple features to improve classification performance. The efficacy of the CCA based methods are compared using individual features and combinations of the best feature subsets. For fusion methods, due to the large dimensions of the fused feature data sets, Principal Component Analysis and 2-Stage CCA/KCCA methods are further considered in this study. The results indicate that overall, kernel CCA methods perform better than linear CCA methods, while being much more computationally intensive. Moreover, the results shows that the fusion based methods outperforms any of three individual features that are considered in this study.