Banknote Verification using Image Processing Techniques

Abstract

Automatic verification of the graphical data plays an increasingly essential role in the global financial system. Verification of banknote is a challenging problem for a human being to check the genuine currencies correctly. In dealings, generating counterfeit banknotes causes loss to the banks, individual, money exchange companies and many more. It has become tough for a human being to verify the currencies easily and appropriately. To avoid counterfeit banknotes, every nation-state comprises numerous kinds of security features such as watermark, flag, micro-printing and many more. Limited banknote verification models have been proposed in the past. Though, the earlier models suffer from a number of limitations which place strong obstacles to the real world banknote data sets. There is a dire need for a reliable technique to detect fake banknote. Based on this evaluation, the new framework is proposed to help the human being to discriminate between genuine and counterfeit banknotes.  The proposed technique is based on the statistical features of a banknote such as color, shape, texture. The selective set of features is extracted with the help of Gray Level Co-occurrence Matrix (GLCM) and later on features are optimized using Principal Component Analysis (PCA). After extracting the set of features, three machine learning classifiers is applied to check the performance of banknote namely Decision Tree, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The experiment results outperform the accuracy of proposed method.