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Civilian American and European Surface Anthropometry Resource (CAESAR) (Robinette, et al., 2002) is an anthropometric database containing the latest civilian population survey of three countries representing the NATO countries: the United States of America, The Netherlands, and Italy. There are many potential applications for the CAESAR database in the anthropometry, ergonomics, and biometrics fields because it provides individual and standardized one-mode data instead of summarized population information such as percentiles. However, people are not using CAESAR more frequently because it is too difficult to access at present. To facilitate the sharing of this valuable resource, the CARD Lab (Computerized Anthropometric Research and Design Laboratory) in the Air Force Research Laboratory has been developing a web application, ARIS (Anthropometry Research Information Systems), to offer CAESAR data search and analysis as well as raw data visualization and extraction.

This research examines and compares methods for mathematically coding 3-D human shapes for shape searching in large databases. The mathematical coding is called shape description and the codes themselves are called shape descriptors. The measure of effectiveness of the shape descriptors is the ability to locate a different copy of the same person from amongst a database of thousands. The hypothesis for this study is that the automated method for deriving a compact shape descriptor developed by Paquet and Rioux (6), referred to henceforth as the Paquet Shape Descriptor (PSD), is an objective shape descriptor that can distinguish between some groups of shapes as well or better than distance type body measurement methods. The present research suggests that PSD excels in searches when body contours are the most important criterion, and offers a viable alternative when key measurement or landmark data are not available.

Traditional and three-dimensional (3-D) scan tools capture anthropometric information in different ways. It is not surprising that measurements from each can have different values. Some possible reasons for differences are tissue compression when using traditional tools, and the difficulty of precise replication of traditional measures using computer algorithms. Even with precise replication of traditional methods, there may be consistent differences in magnitude that can be compensated for. Once compensation factors have been determined, people who have only traditional tools available can also use measurements extracted from 3-D scan data, and people who use 3-D scans can relate extracted measurements to historical data. With adjustments, the results of either method can be used interchangeably. This research compares the consistency of traditional and 3-D scan anthropometry for a small set of traditional univariate measures.

Percentile values are valuable statistics for representing the extreme ends of a distribution of sizes for a single human body dimension; however, their use is not suitable to every problem. When it is desirable to combine dimensions in order to construct a model of the human body or any of its parts, percentiles can create problems due to the fact that, with the exception of 50th percentiles, percentile values are not additive. This report demonstrates the seriousness of the problems associated with the use of percentiles, and describes and compares an alternative approach for representing human body size variability. This alternative, which utilizes regression equations, offers a solution which is easily accessible and demonstrably an improvement over percentiles for the purpose of creating human models.