Classification of Mexican Paper Currency Denomination by Extracting Their Discriminative Colors

In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote's color is characterized by summing all the color vectors of the image's pixels to obtain a resultant vector, the banknote's denomination is classified by knowing the orientation of the resulting vector within the RGB space. In order to obtain a more precise characterization of paper currency, the less discriminative colors of each denomination are eliminated from the images; the color selection is applied in the RGB and HSV spaces, separately. Experimental results with the current Mexican banknotes are presented.

[1]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  K. A. Semendyayev,et al.  Handbook of mathematics , 1985 .

[3]  Martin T. Hagan,et al.  Neural network design , 1995 .

[4]  Georgios S. Paschos,et al.  Perceptually uniform color spaces for color texture analysis: an empirical evaluation , 2001, IEEE Trans. Image Process..

[5]  Fumiaki Takeda,et al.  Thai Banknote Recognition Using Neural Network and Continues Learning by DSP Unit , 2003, KES.

[6]  Il-Hwan Kim,et al.  New recognition algorithm for various kinds of Euro banknotes , 2003, IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468).

[7]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[8]  Jae-Kang Lee,et al.  Distinctive Point Extraction and Recognition Algorithm for Various Kinds of Euro Banknotes , 2004 .

[9]  Hiroshi Sako,et al.  A Hierarchical Classification Method for US Bank Notes , 2005, MVA.

[10]  M. Kamruzzaman,et al.  A machine vision based automatic system for real time recognition and sorting of Bangladeshi bank notes. , 2008, 2008 11th International Conference on Computer and Information Technology.

[11]  Wei Pan,et al.  Fuzzy-based algorithm for color recognition of license plates , 2008, Pattern Recognit. Lett..

[12]  Hamid Hassanpour,et al.  Using Hidden Markov Models for paper currency recognition , 2009, Expert Syst. Appl..

[13]  Anni Cai,et al.  A reliable method for paper currency recognition based on LBP , 2010, 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content.

[14]  Tae-Hyoung Park,et al.  Image segmentation of UV pattern for automatic paper-money inspection , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[15]  F. P. Ahangaryan,et al.  Persian Banknote Recognition Using Wavelet and Neural Network , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[16]  Xiaodong Yang,et al.  Robust and Effective Component-Based Banknote Recognition for the Blind , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Farid García,et al.  Recognition of Mexican banknotes via their color and texture features , 2012, Expert Syst. Appl..