Morphometrics, the quantitative analysis of shape, is used by craniofacial researchers to study abnormalities in human face shapes. Most of the work in craniofacial morphometrics uses landmark points that are manually marked on 3D face data and processed via a generalized Procrustes analysis. For large data sets this manual process is very time-consuming. Dense sets of pseudo-landmarks have also been proposed and successfully used for classification and clustering, but the two main methods in the literature are very computationally intensive. We have developed a computationally simple method that can compute pseudo-landmark points at different resolutions from 3D meshes of human faces. In this paper, we perform a comparative study employing L1-regularized logistic regression to train a classifier that predicts the sex of 500 normal adult face meshes in order to compare our method to two alternative pseudo-landmark methods and a distance matrix approach.Our results show that our method, which is fully automatic, achieved similar results to the best-scoring methods with no manual landmarking and with much lower computation time. Use of the distance matrix did not improve classification results.