Texture Classification for Fake Indian Currency Detection

Abstract

Currency duplication also known as counterfeit currency is a vulnerable threat on economy. It is now a common phenomenon due to advanced printing and scanning technology. India has always been facing serious problem by the increasing rate of fake notes in the market. To get rid of this problem various fake note detection methods are available around the world and most of these are hardware based and costly. Digital image processing is one of the most common and effective techniques used to distinguish counterfeit banknotes from genuine ones. In the present paper an automated image-based technique is described for the detection of newly issued fake currency by RBI. Security features of banknotes such as watermark, micro-printing etc.,are extracted from the banknote images and then detection is performed using Support Vector Machine (SVM). A new approach is presented in this paper using the bit-plane slicing technique to extract the most significant data from counterfeit banknote images with the application of an edge detector algorithm.. The results are then compared with genuine banknotes and with other techniques for detecting counterfeit notes. Unlike existing research, it was observed that the edges obtained using bit-plane sliced images are more accurate and can be detected faster than obtaining them from the original image without being sliced. Experimental results confirm the effectiveness of the proposed algorithm