¹Ambient Intelligence Lab, CYLAB, Carnegie Mellon University, Pittsburgh, PA, U.S.A.

Correspondence: Yang Cai, Ambient Intelligence Lab, CYLAB, Carnegie Mellon University, 4720 Forbes Avenue, Pittsburgh, PA 15213, U.S.A. Tel: +1412 268 5612; Fax: +1412 268 7675; E-mail: ycai@cmu.edu

This paper was revised based on the published ICCS 2006 paper, "A Privacy Algorithm for 3D Human Body Scans", by Joseph Laws and Yang Cai, in LNCS 3994, Springer

Received 20 January 2006; Revised 21 July 2006; Accepted 14 September 2006.

Abstract

In this paper, we explore a privacy algorithm that detects human private parts in a 3D scan data set. The analogia graph is introduced to study the proportion of structures. The intrinsic human proportions are applied to reduce the search space in an order of magnitude. A feature shape template is constructed to match the model data points using Radial Basis Functions in a non-linear regression and the relative measurements of the height and area factors. The method is tested on 100 data sets from CAESAR database. Two surface rendering methods are studied for data privacy: blurring and transparency. It is found that test subjects normally prefer to have the most possible privacy in both rendering methods. However, the subjects adjusted their privacy measurement to a certain degree as they were informed of the context of security.