INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.8, No.2, Feb. 2016

Automatic Recognition and Counterfeit Detection of Ethiopian Paper Currency

Full Text (PDF, 631KB), PP.28-36


Views:202   Downloads:34

Author(s)

Jegnaw Fentahun Zeggeye, Yaregal Assabie

Index Terms

Ethiopian Currency;Currency Recognition;Counterfeit Detection

Abstract

Currency recognition is a technology used to identify currencies of various countries. The use of automatic methods of currency recognition has been increasing due its importance in many sectors such as vending machine, railway ticket counter, banking system, shopping mall, currency exchange service, etc. This paper describes the design of automatic recognition of Ethiopian currency. In this work, we propose hardware and software solutions which take images of an Ethiopian currency from a scanner and camera as an input. We combined characteristic features of currency and local feature descriptors to design a four level classifier. The design has a categorization component, which is responsible to denominate the currency notes into their respective denomination and verification component which is responsible to validate whether the currency is genuine or not. The system is tested using genuine Ethiopian currencies, counterfeit Ethiopian currencies and other countries' currencies. The denomination accuracy for genuine Ethiopian currency, counterfeit currencies and other countries' currencies is found to be 90.42%, 83.3% and 100% respectively. The verification accuracy of our system is 96.13%. 

Cite This Paper

Jegnaw Fentahun Zeggeye, Yaregal Assabie,"Automatic Recognition and Counterfeit Detection of Ethiopian Paper Currency", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.2, pp.28-36, 2016.DOI: 10.5815/ijigsp.2016.02.04

Reference

[1]A. Ahmadi, S. Omatu, T. Kosaka and T. Fujinaka (2004). "A reliable method for classification of bank notes using artificial neural networks". Artificial Life and Robotics, 8(2): 133–139.

[2]M. Aoba, T. Kikuchi and Y. Takefuji (2003). "Euro banknote recognition system using a three-layered perceptron and RBF networks". Transactions on Mathematical Modeling and Its Applications, 44:99–109.

[3]H. Bay, A. Ess , T. Tuytelaars, L. Gool (2008). "Speeded-Up Robust Features (SURF)", Computer Vision and Image Understanding: Similarity Matching in Computer Vision and Multimedia, 110(3):346–359.

[4]S. Chae, J. Kim, and S. Pan (2009). "A Study on the Korean Banknote Recognition Using RGB and UV Information," in Communication and Networking, Series of Communications in Computer and Information Science, 56:477–484.

[5]T. H. Chia and M. J. Levene (2009). "Detection of counterfeit U.S. paper money using intrinsic fluorescence lifetime," Optics Express, 17(24): 22054–22061.

[6]K. Debnath, S. Ahmed and Md. Shahjahan (2010). "A paper currency recognition system using Negatively Correlated Neural Network Ensemble", Journal of Multimedia, 5(6): 560-567.

[7]F. Garcia-Lamont, J. Cervantes, A. Lopez (2012). "Recognition of Mexican Banknotes via their colour and texture features", Expert Systems with Applications, Vol. 39.

[8]L. Georgieva, T. Dimitrova, N. Angelov (2005). "RGB and HSV Colour Models in Colour Identification of Digital Traumas Images", International Conference on Computer Systems and Technologies, Vol. 12, No.1.

[9]M. Gogoi, S. Ali and S. Mukherjee (2014). "Automatic Indian Currency Denomination Recognition Based on Artificial Neural Network". Proceedings of the 2nd International Conference on Signal Processing and Integrated Networks, Noida, IEEE.

[10]H. Gou, X. Li, X. Li, and J. Yi (2011). "A Reliable Classification Method for Paper Currency Based on LVQ Neural Network," in Advances in Computer Science and Education Applications, 202:243–247.

[11]F. Hasanuzzaman, X. Yang, Y. Tian (2011). "Robust and Effective Component-based Banknote Recognition by SURF Features", Proceedings of the 20th Annual Conference on Wireless and Optical Communications, Newark, NJ, pp. 1-6.

[12]N. Jahangir and A. Chowdhury (2007). "Bangladeshi Banknote Recognition by Neural Network with Axis Symmetrical Masks", In Proc. of 10th International Conference on Computer and Information Technology, 9:1-5.

[13]T. Kagehiro, H. Nagayoshi and H. Sako (2006). "A hierarchical classification method for US bank notes". Transactions on Information and Systems, E89D (7):2061–2067.

[14]W. Kavinda, S. Dhammika (2013). "Bank notes recognition device for Sri Lankan vision impaired community", In Proc. of 8th International Conference Computer Science & Education, Colombo, pp. 609-612. 

[15]Y. Liu, L. Zheng and Y. Huang (2014). "Haar-SVM for Real-Time Banknotes Recognition". Journal of Information and Computational Sciences, 11(12): 4031-4039.

[16]A. Sargano, M. Sarfraz, N. Haq (2013). "Robust features and paper currency recognition system", Proceedings of the 6th International Conference on Information Technology.

[17]F. Takeda and S. Omatu (1995). "High-speed paper currency recognition by neural networks", IEEE Transaction on Neural Networks, 6(1):73-77. 

[18]K. Verma, B. Singh and A. Agarwal (2011). "Indian Currency Recognition Based on Texture Analysis". Proceedings of the Nirma University International Conference on Engineering (NUiCONE), Gujarat, IEEE.

[19]W. Yan, J. Chambers and A. Garhwal (2015). "An Empirical Approach for Currency Identification", Multimedia Tools and Applications, 74: 4723-4733.

[20]C. Yeh, W. Su and S. Lee (2011). "Employing multiple-kernel support vector machines for counterfeit banknote recognition". Applied Soft Computing, 11: 1439-1447.

[21]E. Zhang, B. Jiang, J. Duan, and Z. Bian, "Research on Paper Currency Recognition by Neural Networks", In Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, Vol. 4, 2003.

[22]X. Zhu, M. Ren, "A Recognition Method of RMB Numbers Based on Character Features", In Proc. of 2nd International conference on Information, Electronics and Computer, 2014.