A Privacy Algorithm for 3D Human Body Scans

Joseph Laws, Carnegie Mellon University
Yang Cai, Carnegie Mellon University

CMU-CyLab-06-001

Abstract or Description

In this paper, we explore a privacy algorithm that can detect human private parts in a 3D scan dataset. The intrinsic human proportions are applied to reduce the search space an order of magnitude. A feature shape template is constructed to fit the model data points using Radial Basis Functions in a non-linear regression. The feature is then detected using the relative measurements of the height and area factors. The method is tested on 100 datasets from CAESER database