Detection of counterfeit coins based on 3D height-map image analysis. (15th July 2021)

• Record Type: Journal Article
• Authors: Khazaee, Saeed; Sharifi Rad, Maryam; Suen, Ching Y.
• Is Part Of: Expert systems with applications. Volume 174(2021)
• Publication Date: 2021-07-15
• Subjects: Counterfeit coin detection -- 3D precipice borders -- Ensemble classifier -- Height-map images; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114801
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Creating six height-map image datasets for fake coin detection. Proposing 3D Precipice Border Detection Algorithm for detecting 3D borders. No need for image restoration for degraded images in this method. Extracting features with a high discriminating capability. Feeding an ensemble classifier by feature matrices instead of feature vectors. Abstract: Detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. Edge detection is one of the most widely used techniques to extract features from 2D images. However, in 2D images, the height information is missing, losing the hidden characteristics. In this paper, we propose a 3D approach to detect and analyze the precipice borders from the coin surface and extract significant features to train an ensemble classification system. To extract the features, we also propose Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. The proposed method is robust even against degradation which appears on shiny coins after 3D scanning. Therefore, there is no need to restore the degraded images before the feature extraction process. Here, the system has been trained and tested with four types of Danish and two types of Chinese coins. We take advantage of stack generalization to classify the coins and add the reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors. The accuracy obtained by testing Danish 1990, 1991, 1996, and 2008 datasets are 98.6%, 98.0%, 99.8%, and 99.9% respectively. In addition, results for half Yuan Chinese 1942 and one Yuan Chinese 1997 were 95.5% and 92.2% respectively.