An intelligent system for paper currency verification using support vector machines

In recent years, with the advent of digital imaging technology, e.g., color printers and color scanners, it has become easier for counterfeiters to produce fake banknotes. The spread of counterfeit money causes loss to everyone involved in financial transactions. Therefore, an effective and reliable verification technique is necessary for successful and reliable financial transactions. This paper presents a cognitive computation-based technique for paper currency verification. In this regard, Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) analyses of counterfeit, and genuine banknotes were performed. This experimentation confirmed that the materials used in preparation of genuine and counterfeit banknotes were totally different from each other. Based on these findings, a set of discriminative and robust features was proposed to reflect these differences in currency images. The proposed features represented characteristics of the materials of the banknote, such as printing ink, chemical composition, and surface coarseness. With these robust features, Support Vector Machines (SVMs) were employed for classification. In order to evaluate the performance of the proposed technique, experimentations were performed on a self-constructed dataset of Pakistani banknotes, comprised of 195 currency images, including 35 counterfeit banknotes. The results showed that the proposed system achieved 100% verification ability for properly captured images. (C) 2019 Sharif University of Technology. All rights reserved.