Statistical edge-based feature selection for counterfeit coin detection

The number of counterfeit coins released into circulation is persistently increasing. According to official reports, the mass majority of these coins are circulated in the European Union member countries. This paper presents a robust method for counterfeit coin detection based on coin stamp differences between genuine and counterfeit coins. A set of measures based on edge differences are proposed in this paper. The proposed method compares the edge width, edge thickness, number of horizontal and vertical edges, and total number of edges between a test coin and a set of genuine reference coins. The method extends the measures to generate a defect map by subtracting the test coin image from the reference coins to count the number of pixels in small regions of the coin. Additionally, the Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), and Structural Similarity (SSIM) which are well-known measures to track the differences between two images are also applied to the coin image. The sets of features are then placed into index space where each vector represents the features of one test coin and a reference coin. The final feature vector represents the features set of one test coin and is computed by averaging the feature value of vectors in the index space. This feature vector is used to train a classifier to learn the edge feature differences between the two classes. The proposed method achieved precision and recall rates as high as 99.6% and 99.3% respectively, demonstrating the effectiveness and robustness of the selected edge features in authenticating coins. The method was evaluated on a real-life dataset of Danish coins as part of a collaborative effort.

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