The use of anatomical landmarks spans a diverse set of applications because they are essential for understanding the human body. Several research studies have examined the correlation between body shape variations and human performance. Anatomical landmarks are useful for taking anthropometric measures that can be used to characterize body geometries that relate to human performance. In this thesis, we compare parametric models of the human body that were developed from two machine learning methods - Convolutional Neural Network (CNN) and the Lasso Regression Model, to serve as tools for scalable anthropometric measurement. The models were trained on two publicly available labeled body scan datasets: Civilian American and European Anthropometry Resource (CAESAR) and Shape Retrieval Contest (SHREC). The models were used to localize human body landmarks in several poses. This work provides a scalable approach for collecting anthropometric measures.