Engineering & Technology

Engineering & Technology

The Fast Fake Currency Note Detector with Minimum Valid Features

Pages: 12  ,  Volume: 4  ,  Issue: 1 , May   2018
Received: 13 May 2018  ,  Published: 19 May 2018
Views: 107  ,  Download: 64

Authors

# Author Name
1 Gaur Sanjay B. C.
2 Kavita Soni

Abstract

This paper presents a fast and efficient approach for the fake currency note detection with minimum number of features. Typically, this approach evaluates the performance of the system for minimum number of features required to check the Indian Currency Note. This system first check the currency note by its content with minimum number of features then, validate it by some unique features from the guidelines issued by Reserve Bank of India (RBI). The use of minimum number of features, which are essential to check valid currency note, decreases the computing complexity of the system. The system has been tested for ten different denominations of valid and fake currency note both. Experimental results demonstrate the success rate of Fast Fake Currency Note Detector (FFCND) by including different types of features

Keywords

  • Fake Currency Note; Indian Currency Features; FFCND; Image Segmentation; Feature extraction; Content Based Image Retrieval
  • References

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    Hassanpour, Hamid, A. Yaseri, and G. Ardeshiri. "Feature extraction for paper currency recognition." In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on, pp. 1-4. IEEE, 2007.

    Hassanpour, Hamid, and Payam M. Farahabadi. "Using Hidden Markov Models for paper currency recognition." Expert Systems with Applications 36, no. 6 (2009): 10105-10111.

    Sarfraz, Muhammad, ed. Computer-aided intelligent recognition techniques and applications. John Wiley & Sons, 2005.

    Chang, Chin-Chen, Tai-Xing Yu, and Hsuan-Yen Yen. "Paper currency verification with support vector machines." In Signal-Image Technologies and Internet-Based System, 2007. SITIS'07. Third International IEEE Conference on, pp. 860-865. IEEE, 2007.

    Hasanuzzaman, Faiz M., Xiaodong Yang, and YingLi Tian. "Robust and effective component-based banknote recognition for the blind." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(6), pp. 1021-1030, 2012.

    Santhanam, Kamesh, Sairam Sekaran, Sriram Vaikundam, and Anbu Mani Kumarasamy. "Counterfeit currency detection technique using image processing, polarization principle and holographic technique." In Computational Intelligence, Modelling and Simulation (CIMSim), 2013 Fifth IEEE International Conference on, pp. 231-235, 2013.

    J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih, “Image indexing using color correlograms,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp.762–768, Jun. 1997,.

    Young Deok Chun, Nam Chul Kim, “Content-Based Image Retrieval Using Multiresolution Color and Texture Features”, IEEE Trans. On Multimedia, 10(6), October 2008.

    Roy, Ankush, Biswajit Halder, and Utpal Garain. "Authentication of Currency Notes through Printing Technique Verification." In Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, pp. 383-390, 2010.

    Ryszard S. Chora´s, “Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems”, International Journal of Biology and Biomedical Engineering, Vol. 1(1), 2007

    Tianhorng Chang and C.-C. Jay Kuo, “Texture Analysis and Classification with Tree- Structured Wavelet Transform”, IEEE trans. on Image processing, vol. 2(4), October 1993

    S. Conseil , S. Bourennane  and L. Martin, “Comparison Of Fourier Descriptors And Hu Moments For Hand Posture Recognition”, (EUSIPCO 2007), Poznan, Poland, September 3-7, 2007

    M.-K. Hu, “Visual pattern recognition by moment invariants,” IEEE trans. on Information Theory, vol. 8, pp. 179–187, 1962.

    Bharadwaj, Kushik and Gaur, Sanjay "A Zernike Moment based Modified CBIR System with Canny Edge Detector.", International Journal of Computer Applications (0975 – 8887), 17-22, 2014.