DocumentCode :
2371097
Title :
The effects of clothing on gender classification using LIDAR data
Author :
McCoppin, R. ; Rizki, M. ; Tamburino, L. ; Freeman, Alison ; Mendoza-Schrock, O.
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
fYear :
2012
fDate :
25-27 July 2012
Firstpage :
134
Lastpage :
139
Abstract :
In this paper we describe preliminary efforts to extend previous gender classification experiments using feature histograms extracted from 3D point clouds of human subjects. The previous experiments used point clouds drawn from the Civilian American and European Surface Anthropometry Project (CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International). This database contains approximately 4,400 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. The recognition results with the tightly control CAESAR database reached levels of over 90% accuracy. A smaller secondary point cloud data set was generated at Wright State University to allow experimentation on clothed subjects that was not possible with the CAESAR data. We present the preliminary results for the transition of classification software using different combinations of training and tests sets taken from both the CAESAR and clothed subject data sets. As expected, the accuracy achieved with clothed subjects fell short of the earlier experiments using only the CAESAR data. Nevertheless, the new results provide new insights for more robust classification algorithms.
Keywords :
feature extraction; image classification; optical radar; radar computing; 3D point clouds; AFRL Human Effectiveness Directorate; Air Force Research Laboratory; CAESAR anthropometric database; Civilian American and European Surface Anthropometry Project; LIDAR data; SAE International; Wright State University; classification software; clothed subject data sets; clothing effects; cylindrical shapes; feature extraction; feature histograms; gender classification; high-resolution LIDAR whole-body scans; human subjects; invariant histogram features; robust classification algorithm; secondary point cloud data set; LIDAR point cloud; evolutionary computation; feature selection; gender classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Aerospace and Electronics Conference (NAECON), 2012 IEEE National
Conference_Location :
Dayton, OH
ISSN :
0547-3578
Print_ISBN :
978-1-4673-2791-6
Type :
conf
DOI :
10.1109/NAECON.2012.6531043
Filename :
6531043
Link To Document :
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