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Proceedings Paper

Evolving point-cloud features for gender classification
Author(s): Brittany Keen; Aaron Fouts; Mateen Rizki; Louis Tamburino; Olga Lisvet Mendoza-Schrock
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Paper Abstract

In this paper we explore the use of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the 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. When point cloud densities are reduced to levels that might be obtained using stand-off sensors; gender classification accuracy degrades. We introduce an evolutionary algorithm to optimize the number and size of the cylinders used to define histogram features. The objective of this optimization process is to identify a set of cylindrical features that reduces the error rate when predicting gender from low density point clouds. A wrapper approach is used to interleave feature selection with classifier evaluation to train the evolutionary algorithm. Results of classification accuracy achieved using the evolved features are compared to the baseline feature set defined by human experts.

Paper Details

Date Published: 19 May 2011
PDF: 11 pages
Proc. SPIE 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V, 80590N (19 May 2011); doi: 10.1117/12.884764
Show Author Affiliations
Brittany Keen, Wright State Univ. (United States)
Aaron Fouts, Wright State Univ. (United States)
Mateen Rizki, Wright State Univ. (United States)
Louis Tamburino, Wright State Univ. (United States)
Olga Lisvet Mendoza-Schrock, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8059:
Evolutionary and Bio-Inspired Computation: Theory and Applications V
Misty Blowers; Teresa H. O'Donnell; Olga Lisvet Mendoza-Schrock, Editor(s)

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