Evolving Robust Gender Classification Features for CAESAR Data

In this paper we explore the robustness of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the Civilian American and European Surface Anthropometry Resource Project (CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International). This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Features are extracted from each point cloud by embedding the cloud in series of cylindrical shapes and computing a point count for each cylinder that characterizes a region of the subject. These measurements define rotationally invariant histogram features that are processed by a classifier to label the gender of each subject. Preliminary results using cylinder sizes defined by human experts demonstrate that gender can be predicted with 98% accuracy for the type of high density point cloud found in the CAESAR database. In our previous has shown that when point cloud densities are reduced to levels that might be obtained using stand-off sensors; gender classification accuracy degrades. In this paper we show the results of how the classification accuracy degrades as a function of center of mass displacements.