Counterfeit Money Detection Algorithm using Non-Local Mean Value and Support Vector Machine Classifier


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 1, pp. 55-64, Jan. 2013
10.3745/KTSDE.2013.2.1.55,   PDF Download:

Abstract

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original of counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from pervious researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better the previous researches.


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Cite this article
[IEEE Style]
H. Y. Lee and S. K. Ji, "Counterfeit Money Detection Algorithm using Non-Local Mean Value and Support Vector Machine Classifier," KIPS Transactions on Software and Data Engineering, vol. 2, no. 1, pp. 55-64, 2013. DOI: 10.3745/KTSDE.2013.2.1.55.

[ACM Style]
Hae Yeoun Lee and Sang Keun Ji. 2013. Counterfeit Money Detection Algorithm using Non-Local Mean Value and Support Vector Machine Classifier. KIPS Transactions on Software and Data Engineering, 2, 1, (2013), 55-64. DOI: 10.3745/KTSDE.2013.2.1.55.