Combination of spatially enhanced bag-of-visual-words model and genuine difference subspace for fake coin detection

Fake coins are harmful for society, the detection of which is of paramount importance. Due to the large quantities of fake coins in the real world, it is impossible to examine them manually. To address this issue, we present an intelligent system to automatically detect fake coins based on their images. The intelligent system consists of two components: coin image representation and classifier learning. To represent the coin image, a new spatially enhanced bag-of-visual-words model, called SEBOVW model, is proposed. Afterwards, we improve the representation by building a genuine difference subspace. The coin is finally represented based on its projection onto this subspace. In order to discriminate between genuine and fake coins, we train a classifier using the subspace representations. A thorough evaluation of the proposed intelligent system has been conducted on four coin datasets, consisting of thousands of coins of different denominations and from two countries. Promising experimental results in excess of 98% accuracy demonstrate its effectiveness and validity.