Recognition and Verification System for Paper...

Department of Computer Science

Recognition and Verification System for Paper

Currency

Allah Bux

1


Outline Introduction

Motivation

Related work

Problem Statement

Proposed Technique for Recognition

Proposed Technique for Verification

Results and Discussions

Future Works

References

2


Motivation/Application…

Recognition and Verification Systems

have wide range of applications in :

ATM Machines

Auto-Seller Machines

Money Exchange Agencies

Bank Cash Counters

Shops /Hotels etc..

3


Motivation/Application

With the development of modern banking services, and

auto-seller machines, automatic methods of paper

currency recognition and verification are inevitable for

reliable financial transactions .

The need for automatic banknote recognition and

verification systems has motivated many researchers to

develop reliable techniques.

While developing techniques for such systems, two

important parameters to be considered are :

Accuracy

Performance

4


Related work..

Currency Recognition

Hamid Hassanpour et. al., Expert Systems with Applications,

Elsevier, 2009, proposed a technique for currency recognition of

different countries, based on HMM, using the size, color

histogram, and texture based features of whole image. They

achieved 98% accuracy on dataset of 150 banknotes.

Kalyan Kumar Debnath et al. Journal of Multimedia, 2010, used

Negatively Correlated Neural networks Ensembles (ENN) for

Bangladeshi currency recognition. They used compressed image of

125x80 as input to ENN. They achieved 100% accuracy of their

proposed system.

5


Related work..


Takeda et. al. IEEE Conference ,1999 proposed a paper

currency recognition method for US dollars by using small size

neural networks using optimized mask with GA, and achieved up to

98% accuracy .

Takeda et. al. IEEE transactions on Neural Networks 1995,

proposed a currency recognition technique for Japanese currency .

They extracted the currency characteristics and reduced the input

scale by using random mask . They used three layer feed-forward

NN for classification . They achieved more than 92% accuracy .

Takeda et. al. , springer –Verlag Berlin Heidlberg 2003, Kochi

university Japan , developed a currency recognition for Thai

Banknotes . They extract slab values from the currency using Mask

process, and applied NN for classification . They achieved 99.45 %

accuracy

6


Related work..


Junfang et. al. , IEEE conference , 2010, Bejing university , proposed a

technique for Chinese currency recognition based on the local Binary

pattern method by dividing the whole image into mxn blocks , then used

template matching technique for classification .

Euison et. al IEEE Conference 2006, proposed a currency recognition

technique for Korean banknotes . They extracted features using wavelet

transform and applied Canonical Analysis (CA) on the extracted features .

They used Euclidean distance for similarity measure , and achieved 99%

accuracy on three kinds of Korean banknotes.

7


Related work..

Currency Verification

Chi-Yuan et al. Applied Soft computing, Elsevier 2011, Presented

a system based on multiple-kernel support vector machines for

counterfeit banknote verification . Each note is divided into partitions

and histograms of luminance part of (YIQ color space )are taken as

the input to the SVM. They perform experimentation on Taiwanese

Banknote , and achieved up to 100% accuracy .

A. Villa et. al. Analytica Chimica Acta, Elsevier, 2006, proposed a

technique to distinguish the original and fake euro banknotes . The

proposed techniques is based on the ATR infrared spectroscopy

technique, based on analysis of different areas of the banknote. They

performed experimentation on 50 € and 100€ banknotes . They

achieved up to 100% accuracy .

8


Related work..

Currency Verification

Dr. Kenji Yoshida et. al. IEEE 2007, proposed a machine vision

based system for real time detection of the counterfeit Bangladeshi

banknotes . The proposed system works for one hundred and five

hundred taka , relies on the specific features of these two banknotes .

These features are captured with grid scanner . The success rate of the

system is 100% with properly captured images .

Angelo Frosini et. al., IEEE Transaction on Neural Networks,

1996, used low-cost sensor for feature extraction and the employed

Neural networks to develop a technique for paper currency recognition

and verification for Italian currency, photocopies of the currency

were used as counterfeit samples .

9


Statement of the Problem

To develop an intelligent system for recognition

and verification of the paper currency by using

different approach(es) than the existing ones. The

ultimate objective is to yield better results both in

terms of accuracy and performance.

10


Proposed Techniques

Recognition and Verification System for Paper Currency

consists of two modules:

Currency Recognition Technique

Currency Recognition technique based on Neural

Networks and different monetary Characteristics of the

Pakistani banknote has been proposed .

Currency Verification Technique

Currency Verification technique based on SVM and

texture roughness of the banknote has been proposed for

Pakistani Currency .

11


Proposed Technique for Recognition..

Overview of the Recognition System

12



1. Banknote collecting and scanning

We have prepared the database of 350 Pakistani

banknotes ,which includes seven classes (10,

20,50,100,500,1000,and 5000 rupees) .These banknotes

have been scanned with the following settings :

Scanner Type: HP

Resolution : 200ppi , 24-bit picture scan mode

Image Type : jpeg

No. of Banknotes scanned :350, including clean and

noisy banknotes

13



2. Image preprocessing

Preprocessing step can significantly improve the performance of

recognition system.

It is essential for the recognition of worn, torn, and noisy currency

images

We have used pixel wise Wiener adaptive filter for removing noise

from the banknote .

It estimates local mean and variance around each pixel as given below:

Mean Variance

The Wiener filter is created from the above estimation as follows :

14



3. Feature Extraction

The success of any recognition system mainly depends on the proper

feature selection and extraction mechanism .

We have carefully selected a set of features mainly from the list of

security features declared by the issuing authority of the banknotes.

At first step , the feature extraction algorithm considers the size of the

banknote if it is within acceptable range , then following features

would be extracted .

3.1 Aspect ratio of the banknote

3.2 Set of effective color features

3.3 Binary pattern of “Lettering” block of the banknote

3.4 Binary pattern of “See through” block of the banknote

3.5 Binary pattern of “Identification mark” block of the banknote

Security Features Size of the Banknotes

15

State Bank of Pakistan - The Central Bank.mht

Size of the banknotes.docx



3.1 Aspect Ratio of the image

AR=Height/Width

3.2 Set of effective color features ( Color features in I1I2I3 space )

1. I1=(R+G+B)/3

2. I2=(R-B)/2 or I2= (B-R)/2

3. I3= (2G-R-B)/4

3.3 Lettering Block

Lettering is one of important security features indicated by the state bank. This is a denomination appears in Urdu numeral at right top of the banknote, showing the value of the banknote.

Feature Extraction Algorithm Feature database

16

currency recognition algorithm.docx

../Final Feature Set for recognition


Proposed Technique for Recognition..3.4 See Through Block

This is also one of the security features highlighted by the state bank. This is the

value figure of the banknote that appears partly on the obverse left top and

partly on reverse right top can be seen completely when viewed through light

3.5 Identification Mark Block

There are two raised tactile circles or lines at left bottom side of the banknote

which enable the visually impaired persons to recognize the denomination of the

banknote.

17



4. Classification using Neural Networks

4.1 Backpropagation Neural network Structure

18



4.2 Neural network Training /Learning

Training dataset consists of 175 images , including all (10, 20,

50,100,500 , 1000, and 5000) rupees banknotes .

Three layer feed forward Back propagation Neural Network was

trained with the following structure and learning conditions.

No. of Banknotes 175

No. of Banknote types 7

No. of hidden Neurons 30

No. of Inputs 10

No. of output Neurons 7

Maximum No. of iterations 1000

19



4.4 Training Results

Confusion Matrix of the Training results , where classes are represented from 1 to 7

, where 1 represents PKR 10 and 7 represents PKR 5000 banknotes respectively .

20




Following figure shows the performance of Training , Validation and Test .

21




Following figure shows the regression of the training results .

22


Proposed Technique for Recognition..4.3 Neural network Testing

After the NN learning was completed , the trained network was saved

on the system, and 175 banknotes of different denomination are tested

to evaluate the performance of the system .

The testing data set consists of clean, noisy, and hand writing

banknotes. It includes all kinds of banknotes

10,20,50,100,500,1000,and 5000)

To measure the Recognition Ability of the system following formula is

used .

RA = (Number of correctly recognized banknotes ) x100

(Total number of banknotes evaluated)

Noisy Banknotes

23

noisy banknotes


Proposed Technique for Recognition..4.4 Test Results

Following table shows the test results of banknotes passed to the system class wise

Banknote Type Total No. of

banknotes tested

No. of banknotes

correctly recognized

Recognition

ability

10 PKR 10 10 100%

20 PKR 10 10 100%

50 PKR 28 28 100%

100 PKR 26 26 100%

500 PKR 28 28 100%

1000 PKR 38 38 100%

5000 PKR 11 11 100%

RA=151/151*100 = 100%

24



4.4 Test Result

Confusion Matrix given below shows the results of 175 test images passed to the

system at a time .

25



4.4 Test Results

Following figure shows the results of regression of 175 banknotes

passed to the system at a time .

26


Proposed Technique for Verification..

With the advancement of printing technologies , it has been increasingly easier to

produce counterfeit banknotes .

Standard Verification Features

1. Watermark

2. Protective fibers

3. Security thread

4. The code on the security thread

5. Metallic ink

6. Latent image

7. Microprinting

8. Corresponding designs

9. Image with variable color

According to the report of central Bank of Russia the proportion of the

counterfeited features is as follows :

1. Watermark in 95% cases

2. Protective fibers in 95% cases

3. Security thread in 75% cases

4. Text on the security thread in 87% cases

5. Microprinting in 70% cases Genuine/Counterfeit

27

genuine vs. counterfeit.docx



Overview of the Verification System

28


SEM imaging and XRD Analysis of genuine and counterfeit banknotes

29

Genuine

Genuine

Counterfeit

Counterfeit


SEM imaging and XRD Analysis of genuine and counterfeit banknotes

30

Genuine

Counterfeit



1. Feature Extraction

We have selected the two types of features for currency verification.

1.1 Statistical Information

The statistical information of material, printing ink , thickness of the

printing paper , and ingredients used in banknote preparation.

1.2 Surface/Texture Roughness Features

These features reveal the information regarding roughness of the

surface .

1. 1 Finding statistical Information

when a light is sent to the rough surface of the banknote, some part of

the light is reflected back, while other part of the light is refracted,

means its direction is changed but it still passes through the banknote .

31



During refraction process, if the intensity is Io and the intensity of the

refraction of light is I then the relationship is [Tang chunhui].

Where α is the medium absorption coefficient, η is the reflection

coefficient , and d is thickness of the medium

From the above equation we can understand that each pixel value is

related to the basic characteristics of currency image , including

reflection coefficients , refraction coefficients , and absorption

coefficient of an image.

In fact, this is reflection of material, thickness of the paper, ingredients,

printing ink, printing method and techniques employed for banknote .

- d

0I=I (1- )e

32



Since the margin of difference of genuine and counterfeit banknotes

vary in different parts of banknotes , we have divided the banknote

vertically, into five different regions .

At first step , we have calculated the derivative of currency image as

follows :

We have selected five parameters related to statistical information .

1. D_mean: Derivative image mean , average of all pixels in a selected block,

expressed as :

2. D_min: Derivative image minimum value , average of minimum value in

each row or column of the image .

( , )f x y ( , )

( , )

( , )( , )

x

y

f x yf x y

x

f x yf x y

y

1 1

1_ ( , )

m n

mean

i j

D f i jmn

1

1_ min min( ( , ))

n

i

D f x in

33



3. D_max: Derivative image average of maximum value in each row or

column of block .

4. D_var : Variance , The arithmetic average of the squared differences

between the values and the mean.

5. D_cov : Covariance matrix , or matrices, where each row is an

observation, and each column is a variable, cov(X) is the covariance

matrix

1

1_ max max( ( , ))

n

i

D f x in

2

1

1_ var ( )

n

i i

i

D f xn

cov( , )_ cov

i j

i j

X XD

34



Create a map from by looking at the transitions of the current

pixel to previous pixel both in x and y direction for each block. We have

divided the transitions into 8 different groups as summarized in the table

given below:

1.2 Calculating roughness by peak and valley points

Current pixel I(r,c)

Previous -xI(r,c-1)

Previous -yI(r-1,c)

Group name

+Ve -Ve -Ve Peak

+Ve -Ve +Ve Partial Peak-yx

+Ve +Ve -Ve Partial peak-xy

+Ve +Ve +Ve Ramp down

-Ve -Ve -Ve Ramp up

-Ve -Ve +Ve Partial Valley- yx

-Ve +Ve -Ve Partial valley-xy

-Ve +Ve +Ve Valley

( , )f x y

35



Count the total number of pixels in each group and sum

up these pixels

Once you have the sum and count of pixels in each group

, you can calculate the mean of each group of pixel in a

block.

All these features are used as input to the SVM for

classification

(a) 1000 Genuine Banknote (b) 1000 counterfeit banknote

Roughness Feature Extraction Algorithm

36

Rougness_algorithm.txt



2.1 Support Vector Machine (SVM) training/learning

Definition Define the hyper-plane H such that:

xi•w+b ≥ +1 when yi =+1

xi•w+b ≤ -1 when yi =-1

H1 and H2 are the planes:

H1: xi•w+b = +1

H2: xi•w+b = -1

The points on the planes H1 and H2 are the Support Vectors

d+ = the shortest distance to the closest positive point

d- = the shortest distance to the closest negative point

The margin of a separating hyper-plane is d+ + d-.

37



2.1 Support Vector Machine (SVM) training/learning

SVM classifier has been used for currency verification problem. We

have build a separate classifier for each class of banknotes.

Training dataset consists of 125 images , including 100 genuine

banknotes of (500 , 1000, and 5000) , and 25 fake banknotes of

(500,1000,and 5000).

SVMs are trained with following structure and learning conditions.

No. of Banknotes 125

Types of Banknote 3(500,1000,5000)

No. of genuine Banknotes 100

No. of Counterfeit Banknotes 25

No. of Inputs 93

No. of outputs 2 (0/1)

38


Proposed Technique for Verification..(a) Confusion matrix of training 5000 banknote (b) Regression diagram of 5000 banknote

with 15 genuine and 5 counterfeit banknotes

39


Proposed Technique for Verification..(a) Confusion matrix of training 1000 banknote (b) Regression Diagram of 1000 banknote


40


Proposed Technique for Verification..(a) Confusion matrix of training 500 banknote (b) Regression Diagram of 500 banknote


41


Proposed Technique for Verification..2.2 SVM Testing Results

Once the training process is complete for all kinds of banknotes, the trained

networks are saved on the system, and tested to evaluated the performance

of the system .

The test results are shown in the following table .

Classifier Banknote Type Total No. of

banknotes tested

No. of banknotes

correctly

recognized

Recognition

Ability

C1 500 PKR 20 (Genuine) 20 100%

500 PKR 4 (Fake) 4 100%

C2 1000 PKR 30 (Genuine) 30 100%

1000 PKR 3 (Fake) 3 100%

C3 5000 PKR 10 (Genuine) 10 100%

5000 PKR 2 (Fake) 2 100%

42

RA=69/69*100 = 100%


Future Work

The future work includes:

Verification of banknotes using infrared and ultraviolet

features.

Verification using the spectroscopy/Microscopy technique.

Recognition and verification of banknote from both sides

and orientations .

Installation and implementation of system on DSP unit for

commercial use .

43


Published/Submitted Papers

44

1. Allah Bux, Muhammad Sarfraz, Nuhman Ul Haq, An Intelligent System for Paper Currency

Recognition with Robust Features, Journal of Intelligent & Fuzzy Systems [Published ,

IF =0.936]

URL: http://iospress.metapress.com/content/w561661l27735181/

2. Muhammad Sarfraz, Allah Bux, , Nuhman Ul Haq, An Intelligent System for Paper Currency

Verification using Support Vector Machines, The Imaging Science Journal [Under review,

IF 0.575]

3. Allah Bux, Muhammad Sarfraz, Nuhman Ul Haq, Robust features and Paper Currency

Recognition System, The 6th International Conference on Information Technology, ICIT’13,

Amman Jordan [Published]

URL: http://sce.zuj.edu.jo/icit13/images/Camera%20Ready/Artificial%20Intelligence/671.pdf

http://iospress.metapress.com/content/w561661l27735181/

http://sce.zuj.edu.jo/icit13/images/Camera Ready/Artificial Intelligence/671.pdf


References[1] Chi-Yuan et. al ,Employing multiple-kernal support vector machines for counterfeit banknote

recognition , Applied Soft Computing 2010(In press)

[2] Kalyan Kumar Debnath, bSultan Uddin Ahmed, aMd. Shahjahan, A Paper Currency Recognition

System Using Negatively Correlated Neural Network Ensemble, JOURNAL OF MULTIMEDIA,

VOL. 5, NO. 6, DECEMBER 2010.

[3] Vila, A., Ferrer, N., Mantecon, J., Breton, D., & Garcia, J. F. (2006). Development of a fast and

non-destructive procedure original and fake euro notes.

[4] Zhang, E. H., Jiang, B., Duan, J. H., Bian, Z. Z. (2003). Research on paper currency recognition by

neural networks. In Proceeding of the second international conference machine learning and

cybernetics.

[5] M. Gori, A. Frosini and P. Priami. “A neural network based model for paper currency recognition

and verification”, IEEE Trans. Neural Networks, Nov.1996

[5] MS. Trupti and Dr, N.G. Bawane, Feature Extraction parameters for Genuine paper Currency

Recognition & Verification, International Journal of Advanced Engineering Sciences and

Technologies(IJAEST) , 2011

[6] Hamid Hassanpour a,*, Payam M. Farahabadi “Using Hidden Markov Models for Feature

Extraction in Paper Currency Recognition”, Expert Systems with Applications, Vol. 36, No. 6, pp.

10105-10111, 2009

[7] Takeda, F. et. al. “Thai Banknote Recognition and Continues Learning “ , Springer-verlag Berlin

Heidelberg 2003.

[8] Baiqing Sun and Fumiaki Takeda, “Proposal of Neural Recognition with Gaussian Function and

Discussion for Rejection Capabilities to Unknown Currencies” Springer-Verlag Berlin Heidelberg

2004.

45


References..[9] Fumiaki Takeda' and Toshihiro Nishikage, “ Multiple kinds of Paper Currency Recognition using Neural

Network and application for Euro Currency” , IEEE,2000

[10] Fumiaki Takeda, “A Neuro-Paper Currency Recognition Method Using Optimized Masks by Genetic

Algorithm”, IEEE,1995

[11] Fumiaki Takeda and Sigeru Omatu, “A Neuro-Paper Currency Recognition Method Using Optimized

Masks by Genetic Algorithm”, IEEE,1995

[12] Parminder Singh Reel, Gopal Krishan, Smarti Kotwal , “Image Processing based Heuristic Analysis for

Enhanced Currency Recognition”, International Journal of Advancements in Technology, 2011.

[13] Jae-Kang Lee, and 11-Hwan Kim, “New Recognition Algorithm for Various Kinds of Euro Banknotes”

,IEEE,2003

[14] F-HUI KONG1, JI-QUAN MA 2,3, JIA-FENG LIU3, ”PAPER CURRENCY RECOGNITION USING

GAUSSIAN MIXTURE MODELS BASED ON STRUCTURAL RISK MINIMIZATION”,IEEE, 2006.

[15] Baiqing Sun, Jilu Li , “Recognition for the Banknotes Grade Based on CPN”,IEEE, 2008.

[16] Ji Qian, Dongping Qian, and Mengjie Zhang,” A Digit Recognition System for Paper Currency

Identification Based on Virtual Instruments” , IEEE, 2006.

[17] Sigeru Omatu Michifumi Yoshioka, Yoshihisa Kosaka , “Reliable Banknote Classification Using Neural

Networks”, IEEE, 2009

[18] Stefan Glock1, Eugen Gillich1, Johannes Schaede2, and Volker Lohweg1,“Feature Extraction Algorithm

for Banknote Textures Based on Incomplete Shift Invariant Wavelet Packet Transform, Springer-Verlag

Berlin Heidelberg 2009

46


References..[19] Fumiaki Takedaa, *, Toshihiro Nishikage a, Sigeru Omatub, “Banknote recognition by means of

optimized masks, neural networks and genetic algorithms”, Engineering Applications of Artificial

Intelligence (1999).

[20] CAO Bu-Qing et. al., “Currency Recognition Modeling Research Based on BP Neural Network

Improved by Gene Algorithm” IEEE, 2010.

[21] Raihan Ferdous Sajal, Mohammed Kamruzzaman, Faruq Ahmed Jewel, “Machine Vision Based

Automatic System for Real Time Recognition and Sorting of Bangladeshi Bank Notes”, IEEE, 2008.

[22] Ali Ahmadi, Sigeru Omatu, and Toshihisa Kosaka , “A Methodology to Evaluate and Improve Reliability

in Paper Currency Neuro-classifiers” , IEEE , 2003.

[23] Ali Ahmadi, Sigeru Omatu, and Toshihisa Kosaka , “Implementing a Reliable Neuro -Classifier for Paper

Currency Using PCA Algorithm” , SICE, 2002.

[24] Jianbiao He,Zhigang Hu,Pengcheng Xu and Ou Jin, Minfang Peng, “The Design and Implementation of

an Embedded Paper Currency Characteristic Data Acquisition System” , IEEE, 2008.

[25] Li Wenhong , Tian Wenjuan, Cao Xiyan and Gao Zhen , “Application of Support Vector Machine (SVM)

on Serial Number Identification of RMB” , IEEE, 2010.

[26] Junfang Guo, Yanyun Zhao, Anni Cai , “A Reliable Method for Paper Currency Recognition Based on

LBP , IEEE, 2010.

[27] Euisun Choi, Jongseok Lee and Joonhyun Yoon, “Feature Extraction for Bank Note Classification Using

Wavelet Transform”, IEEE, 2006.

[28] Nadim Jahangir and Ahsan Raja Chowdhury , “Bangladeshi Banknote Recognition by Neural Network

with Axis Symmetrical Masks”, IEEE, 2007.

47


References..[29] Dr. Kenji Yoshida1, Mohammed Kamruzzaman2, Faruq Ahmed Jewel3, Raihan Ferdous Sajal4.,

“Design and Implementation of a Machine Vision Based but Low Cost Stand Alone System for Real

Time Counterfeit Bangladeshi Bank Notes Detection” , IEEE, 2007.

[30] Chao He a Mark Girolami a;¤ Gary Ross , “Employing Optimized Combinations of One-Class Classifiers

for Automated Currency Validation” , Preprint submitted to Elsevier Preprint, 2003.

[31] Jianbin Xie, Chengang Qin, Tong Liu, Yizheng He, and Ming Xu, “A New Method to Identify the

Authenticity of Banknotes Based On the Texture Roughness”, IEEE, 2009.

[32] Giuseppe Schirripa Spagnolo, Lorenzo Cozzella and Carla Simonetti, “Banknote security using a

biometric-like technique: a hylemetric approach” , Meas. Sci. Technol. 21 (2010) 055501 (8pp)

[33] Giuseppe Schirripa Spagnolo, Lorenzo Cozzella and Carla Simonetti, “Currency verification by a 2D

infrared barcode” , Meas. Sci. Technol. 21 (2010) 107002 (5pp).

[34] Chin-Chen Chang *, Tai-Xing Yu and Hsuan-Yen Yen, “Paper Currency Verification with Support

Vector Machines”, IEEE, 2008.

[35] OSU SVM toolbox http://svm.sourceforge.net/docs/3.00/api/

48

http://svm.sourceforge.net/docs/3.00/api/


Thanks

49

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