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Reseach Article

Bank Notes Authentication System based on Wavelet Features and Artificial Neural Network

by Ch.Ratna Jyothi, Y.K. Sundara Krishna, V. Srinivasa Rao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 2
Year of Publication: 2016
Authors: Ch.Ratna Jyothi, Y.K. Sundara Krishna, V. Srinivasa Rao
10.5120/ijca2016909694

Ch.Ratna Jyothi, Y.K. Sundara Krishna, V. Srinivasa Rao . Bank Notes Authentication System based on Wavelet Features and Artificial Neural Network. International Journal of Computer Applications. 142, 2 ( May 2016), 24-28. DOI=10.5120/ijca2016909694

@article{ 10.5120/ijca2016909694,
author = { Ch.Ratna Jyothi, Y.K. Sundara Krishna, V. Srinivasa Rao },
title = { Bank Notes Authentication System based on Wavelet Features and Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 2 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number2/24869-2016909694/ },
doi = { 10.5120/ijca2016909694 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:52.598590+05:30
%A Ch.Ratna Jyothi
%A Y.K. Sundara Krishna
%A V. Srinivasa Rao
%T Bank Notes Authentication System based on Wavelet Features and Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 2
%P 24-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Monetary transactions are integral part of our day to day activities, so currency authentication has become one of the active research area at present and it has vast potential applications. In this paper we introduced a system to verify the authentication of banknotes bench mark dataset using computer vision. We considered 1372 samples of various bank notes in our work. The technology of currency authentication aims to search and extract potential features of paper currency for efficient classification. Features were extracted from images that were taken from genuine and forged banknote-like specimens. They are classified using Artificial Neural network. The variance of Wavelet Transformed image (continuous), skewness of Wavelet Transformed image (continuous),curtosis of Wavelet Transformed image (continuous),entropy of image (continuous) features are extracted from images for accurate classification. Our proposed system able to authenticate with zero percent misclassification.

References
  1. Hassanpour H, Farahabadi, PM. Using Hidden Markov Models for paper currency recognition, Expert Systems with Applications 2009; 36(6):10105-10111.
  2. Gonzalez RC, Woods RE. Digital image processing, Prentice Hall; 2009.
  3. Hassanpour H, Mesbah M. Neonatal EEG seizure detection using spike signatures in the time-frequency domain, IJE Transactions A: Basics2007; 20(2):137-146.
  4. Hassanpour H, Yaseri A, Ardeshir G. Feature Extraction for Paper currency Recognition. In International symposium on signal processing and its applications (ISSPA), Sharjah, UAE; 2007.
  5. Iosifescu M. Finite Markov process and their applications. New York, NY: Wiley; 1980.
  6. Jae LS. Two-dimensional signal and image processing. Englewood Cliffs, NJ: Prentice Hall; 1990.
  7. Kim M, Kim D, Lee S. Face recognition using the embedded HMM with second-order block-specific observations. Pattern Recognition2003; 36(11):2723–2733.
  8. Takeda F, Nishikage T. Multiple kinds of paper currency recognition using neural network and application for Euro currency. In IEEE International Joint Conference on Neural Networks, Como, p. 143–147; 2000.
  9. Toussaint GT. Proximity graphs for nearest neighbor decision rules: Recent progress. Proceedings of the 34th Symposium on the INTERFACE, p. 17-20; 2002.
  10. Vila A, Ferrer N, Mantecon J, Breton D, Garcia, JF. Development of a fast and non-destructive procedure original and fake euro notes.Analytica Chimica Acta 2006; 559:257–263.
  11. Zhang EH, Jiang B, Duan JH, Bian ZZ. Research on paper currency recognition by neural networks. In Proceeding of the second international conference on machine learning and cybernetics, p. 2193–2197; 2003.
  12. Liu Q, Tang L. Study of Printing Identification Based on Multi-spectrum Imaging Analysis, Proceedings of the International Conference on Computer Science and Software Engineering, p. 229 – 232; 2008.
  13. Ahmadi A, Omatu S, Yoshioka M. Implementing a Reliable Neuro-Classifier for Paper Currency Using PCA Algorithm, Proceedings of the41st SICE Annual Conference, p. 2466-2488; 2002.
  14. Takeda F, Omatu S. High Speed Paper Currency Recognition by Neural Networks, IEEE Transactions on Neural Networks 1995; 6(1):73-77.
  15. Takeda F, Omatu S, Onami S, Kadono T, Terada K. A Paper Currency Recognition Method by a Small Size Neural Network with OptimizedMasks by GA, Proceedings of the IEEE World Congress on Computational Intelligence, Orlando, USA, 7, p.4243-4246; 1994
  16. Bow, Sing-Tze. Pattern Recognition and Image Preprocessing. New York: Marcel Dekker, Inc., 2002.
  17. Hasanuzzaman FM, Yang X, Tian YL. Robust and effective component-based banknote recognition by SURF features, Proceedings of the20th Annual Wireless and Optical Communications Conference, p. 1-6; 2011.
  18. He J, Hu Z, Xu P, Jin O, Peng M. The design and implementation of an embedded paper currency characteristic data acquisition system,Proceedings of the International Conference on Information and Automation, p. 1021-024; 2008.
  19. Junfang G, Yanyun Z, Anni C. A reliable method for paper currency recognition based on LBP, Proceedings of the 2nd IEEE InternationalConference on Network Infrastructure and Digital Content, p. 359-363; 2010.
  20. Sargano AB, Sarfraz M, Haq N. An Intelligent System for Paper Currency Recognition with RobustFeatures, Journal of Intelligent and Fuzzy Systems 2014; 27(4): 1905 –1913.
Index Terms

Computer Science
Information Sciences

Keywords

Currency recognition Artificial Neural Network Classification wavelets