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.