27 January 2011 Preliminary study of statistical pattern recognition-based coin counterfeit detection by means of high resolution 3D scanners
Author Affiliations +
Abstract
According to the European Commission around 200,000 counterfeit Euro coins are removed from circulation every year. While approaches exist to automatically detect these coins, satisfying error rates are usually only reached for low quality forgeries, so-called "local classes". High-quality minted forgeries ("common classes") pose a problem for these methods as well as for trained humans. This paper presents a first approach for statistical analysis of coins based on high resolution 3D data acquired with a chromatic white light sensor. The goal of this analysis is to determine whether two coins are of common origin. The test set for these first and new investigations consists of 62 coins from not more than five different sources. The analysis is based on the assumption that, apart from markings caused by wear such as scratches and residue consisting of grease and dust, coins from equal origin have a more similar height field than coins from different mints. First results suggest that the selected approach is heavily affected by influences of wear like dents and scratches and the further research is required the eliminate this influence. A course for future work is outlined.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marcus Leich, Marcus Leich, Stefan Kiltz, Stefan Kiltz, Christian Krätzer, Christian Krätzer, Jana Dittmann, Jana Dittmann, Claus Vielhauer, Claus Vielhauer, } "Preliminary study of statistical pattern recognition-based coin counterfeit detection by means of high resolution 3D scanners", Proc. SPIE 7864, Three-Dimensional Imaging, Interaction, and Measurement, 786412 (27 January 2011); doi: 10.1117/12.872360; https://doi.org/10.1117/12.872360
PROCEEDINGS
8 PAGES


SHARE
Back to Top