A three-dimensional shape database from a large-scale anthropometric survey Peng Li, Brian Corner, Jeremy Carson, Steven Paquette US Army Natick Soldier Research Development & Engineering Center, Natick, Massachusetts, USA Abstract: This paper presents an anthropometric database that incorporated both standard body measurements and three-dimensional (3D) scans from US Army Anthropometric Survey (2013). The database provides query abilities based on measurement and 3D shape by example. Keywords: anthropometric database, body shape query, ANSURII 1. Introduction As the use of three-dimensional (3D) scanner has expanded over the past decade, the need for a structured 3D anthropometric database has received increased attention. A number of web based 3D scan viewers [Ressler and Wang 2002] and 3D shape databases [Paquet 2000] have been developed around the Civilian America and European Anthropometry Resource (CAESAR) survey [Robinette et al. 2002]. The World Engineering Anthropometry Resource (WEAR) [http://www.bodysizeshape.com/] was established to address the need of a comprehensive anthropometry database incorporated 3D data [Mollard et al 2006]. The recently completed US Army Anthropometric Survey (ANSURII) collected 3D surface whole body, head and foot scans from approximately 10000 soldiers, in addition to 93 directly measured standard anthropometric measurements [Gordon et al. 2014]. To fully exploit this rich resource, we implemented a 3D shape database that enables users to query ANSURII data using standard anthropometric measurements as well as 3D shapes. The 3D shape database may be queried through a web interface in three categories: 1) query by multiple standard anthropometric measurements, 2) query by an existing shape example (for example, a new scan from a scanning device or an existing scan in the database), and 3) query using a statistically generated shape. The following sections introduce the details of our database implementation. 2. Database contents The database is implemented in MySQL [https://www.mysql.com/] and accessible through a web based browser. Data for each subject contains: o demographics (e.g., age, sex, race/ethnicity), o anthropometric measurements, o 3D whole body scan, o torso shape descriptor, o thumbnail image of the 3D body scan The raw 3D scans from ANSURII survey are very dense point cloud (~500k vertices) with some voids. To obtain a clean, uniform, watertight mesh, we applied a surface alignment tool [Hirshberg 2011] to register raw data into a uniform structure. The result was a whole body mesh with a lower polygon count (11k vertices and 22k faces) whose vertices are in full correspondence. Furthermore, to anonymize the scans we replaced the face with a virtual face. Figure 1 shows a raw scan and its template aligned surface. 1
Figure 1. A raw scan and its template aligned surface Because much of our work is on the development and evaluation of torso-borne clothing and equipment, we created a separate torso form for each subject in the database. Torsos were extracted from a whole body scan by deleting arm, leg, and head vertices at standard cut points. A Discrete Cosine Transform (DCT) was then applied to quantify torso shape [Li et al. 2012]. The DCT allows us to reconstruct the original surface shape, and partitions size information from each torso form. Values of the DCT are as a torso 3D shape descriptor. With the above stated data in the database, we provide three query mechanisms, as shown in Figure 2. The first is query based on demographic data and standard anthropometric measurements as shown in Figure 3. This query named a filter search, returns body measurements and thumbnail images of subjects who meet the demographic and body measurement criteria. Users may also specify to display those measurements not included in the query form. Since our database is geared toward 3D data, all search results include subjects 3D image as a cue for later shape query. Figure 2. Front page of the database 2
Figure 3. Filter search input screen (left) and results displayed (right). The second query is a search by example (Fig. 4). In our current implementation, the query will be on the torso shape descriptor. The user provides an example either from a previous filter search, where a subject was identified based on standard anthropometry and demography, or a mesh from an external source. A neighbourhood count, that is the number of most similar subjects to be returned, is also required. The database returns similar shapes based on their correlation distance to the example subject provided. For a subject that already exists in the database, this query is a simple sorting of a pre-calculated correlation matrix of the torso shape descriptors. If a new scan is submitted, the scan is processed and the shape descriptor computed. The shape descriptor values are then uploaded and the correlation distance between this new shape descriptor to the shapes in the database is calculated on-the-fly. Figure 4. Search by example input (left) and results displayed (right) 3
Figure 5.Distribution of variance from Torso PCA The third query form uses a statistically generated shape as an example. The collection of ANSURII torso 3D shape descriptors forms a shape space. We applied principal component analysis (PCA) to this shape space and decomposed the shape variation into a number of principal axes. We selected the first ten principal components for the shape generator. The ten components account for 77% of torso variance (as shown in fig. 5). For each principal component there is a sliding value from -1 to 1 to control its blending weight for the corresponding extreme shapes chosen from the maximum and minimum standard deviation (+/-3 standard deviations) of that principal component. For example figure 6 shows extreme shapes from the second principal component. The top ten most significant principal components provide twenty extreme shapes as constituent shapes for a shape generator. Figure 6. Extreme shapes of PC2 (front and side view) Using an interactive interface, we may blend across the constituent shapes derived from PCA. In this way a user may generate a novel shape and use it to search for similar shapes in the database population. The shape generator is shown in Figure 7 and some search results are shown in Figure 6. The search result page from the statistically produced shape query process may also be filtered through selected anthropometric measurements that are available for the matched shapes in the database. While a search displays subjects ID, thumbnail images and measurements, 3D geometry of each subject is displayed by clicking on the subject ID. Figure 9 shows a 3D whole body surface viewer from the database. The query results that include anthropometric measurements and 3D scans of relevant subjects can be downloaded to users local computer. Since these downloadable scans have uniform, fully corresponded mesh structure, users can easily use them for further shape analysis and modelling tasks. 4
Figure 7. PCA generated torso shape viewer. Sliders to manipulate the ten PC axis values within a range of +/- 3 standard deviations are shown on the right. Figure 8. Search results from PCA generated torso shape 5
Figure 9. 3D surface viewer 3. Torso shape descriptor detail and shape search method We selected torso 3D shape as our first query candidate because the torso is where most gear is carried, it is a dominant part of a whole body shape the nuance of torso geometry (front and back curves, chest shape, shoulder shape) is not easily captured with linear anthropometric measurements, and the torso is less prone to pose variation during a 3D scanning session. As noted above, for shape-based queries the torso was segmented from whole body scans and parametrised using DCT. The DCT matrix was used in shape matching. The similarity of two shapes is based on cosine distance between two shape descriptors [Li et al. 2012]. Although the shape descriptor has much smaller file size (a 35x35 matrix) than a raw scan, it is still time consuming to do distance calculation for 6000 files. It is also not efficient to implement the distance computation in SQL. Therefore shape search functions were implemented in C language and called through the server side scripting language PHP. When we query shape by subject ID we simply search a pre-calculated correlation matrix. However, for a new uploaded torso shape, the search must conduct a full distance calculation with all stored shape descriptor files, which takes approximately 40 seconds. When a shape is generated from the PCA viewer, it sends weight values w i of ten PCs to the server. On the server side these values are used as PCA parameters to construct a shape descriptor (SD) as shown in following formula [Li et al. 2015]: where λ i is the ith eigenvalue and E i is the ith eigenvector from PCA. Then the distances between SD to stored shape descriptors in the database are calculated and sorted. This is equivalent to an uploaded shape descriptor search. 4. An example query We received a request for female whole body geometry to test the robusticity of software that morphs internal organ models to a given scan. Our goal was to provide enough scans to challenge the software but not too many so the testing could be done in a reasonable amount of time. We began with a PCA of postprocessed 3D adult female scans in the ANSURII database (N=1708). Results from PCA indicated the bulk of the variation, 95%, was accounted for by 10 components. In addition to the mean form, for each of the 10 6
components we selected scans at +/- 1 standard deviation and +/- 3 standard deviations. We then submitted each to a search by example query with a neighbourhood of 5 scans. The query resulted in an overall average scan and 24 scans per component (the original example and 5 neighbors for +/- 1 and +/- 3 standard deviations). The total sample for software testing was 241 scans, which represented a very wide range of adult female whole body variation. 5. Conclusion and future work We have developed a 3D shape centred anthropometric database and made 3D whole body scan data searchable in both the standard anthropometric measurement domain and 3D shape domain, based on a 3D torso shape descriptor. We provided an example how the database may be used to select 3D geometry to test body morphing software. The potential of 3D data for product design and evaluation is still at an early stage. Questions about how to use 3D data will continue for a while until an end user has proper tools to deal with 3D data. For example, for a particular product a designer may be more interested in body surface variation other than the torso. We are working on body segmentation, e.g., head, hands, feet, lower and upper limb. Also, we are adding more common-language anatomical descriptors to help non-specialists.. Acknowledgements The database is implemented through a U.S. Army contract with TSE Inc. (contract # W911QY12C0085) References Gordon, C., Blackwell, C., Bradtmiller, B, Parham, J., Barrientos, P, Paquette, S., Corner, B., Carson, J., Venezia, J., Rockwell, B., Mucher, M. and Kristensen, S, 2014. 2012 Anthropometric Survey of U.S. Army Personnel: Methods and Summary Statistics. Technical Report NATICK/TR-15/007, US Army Soldier Systems Command, Natick Research, Development and Engineering Center Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B., Black, M. J., 2011 Evaluating the automated alignment of 3D human body scans 2nd Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, pp. 76-86, Oct. 25-26, 2011. Li, P., Corner, B., Paquette, S., 2012. Shape description of the human body based on discrete cosine transform, in Advances in Applied Human Modeling and Simulation, CRC Press, Pages 169-178 Li, P., Corner, B., Paquette, S., 2015. Shape analysis of female torso based on discrete cosine transform, (to be published in) International Journal of Clothing Science and Technology, Vol. 27 Iss: 5, Emerald Group Publishing Limited. Mollard, R., Ressler, S., Robinette, K., Database contents, structure, and ontology for WEAR, Proceedings of the 16th Triennial World Conference of the International Ergonomics Association, 2006 Paquet, E., Robinette, K., Rioux, M., Management of three-dimensional and anthropometric databases: Alexandria and Cleopatra, J. Electronic Imaging 01/2000; 9:421-431. Ressler,S., Wang, Q., A Web3D Based CAESAR Viewer, CARS 2002 H.U. Lemke, M.W. Vannier; K. Inamura, A.G. Farman, K. Doi & J.H.C. Reiber (Editors), CARS/Springer. Robinette, K., Blackwell, S., Daanen, H., Boehmer, M., Fleming, S., Brill, T., Hoeferlin, D., Burnsides, D., 2002, Civilian American and European Surface Anthropometry Resource (CAESAR) Final Report, Volume I: Summary, Technical Report AFRL-HE-WP-TR-2002-0169, United States Air Force Research Laboratory 7