In recent years, automatic facial analysis has attracted much interest among computer science researchers in the healthcare and computer vision fields studying facial anthropometric measurements using photographs. However, to date, there have been no healthcare or computer vision publications that use standardized photographs to differentiate features between sub-ethnic groups by leveraging the power of machine learning on two-dimensional computer vision benchmark data sets (2D CVBDs). Thus, the present work is an interdisciplinary study at the interface of healthcare and computer vision fields that attempts to fill this literature gap where we explore the use of machine learning on 2,789 photographs from eleven 2D CVBDs to identify k top discriminative features in major and sub-ethnic groups. These features are ranked based on information gain values and p-values. We also provide a comprehensive analysis of using information-gain-based and p-value-based features. Our machine learning model achieves an accuracy of 96-99%, and our findings reveal that information-gain-based features have the upper hand over p-valuebased features. The top three information-gain-based features in sub-ethnic groups are: dn (distance from the tip of the nose to the center of the mouth), hf (face height) and wn (nose width), while the top three information-gain-based features in major ethnic groups are: de (distance between the inner corners of the eyelids), hf and dn. These results are then compared to the results obtained using standard deep learning techniques such as OxfordNet (VGG16), Residual Networks (ResNet50), and Inception-V3, where accuracy of 90-94% was seen. We hope that these findings will lead to future collaboration between computer vision and healthcare researchers studying facial anthropometric measurement studies.