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Department of Computer Science
Outline Introduction
Motivation
Related work
Problem Statement
Proposed Technique for Recognition
Proposed Technique for Verification
Results and Discussions
Future Works
References
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Department of Computer Science
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..
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Department of Computer Science
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
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Department of Computer Science
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.
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Department of Computer Science
Related work..
Currency Recognition
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
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Department of Computer Science
Related work..
Currency Recognition
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.
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Department of Computer Science
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 .
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Department of Computer Science
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 .
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Department of Computer Science
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.
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Department of Computer Science
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 .
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Department of Computer Science
Proposed Technique for Recognition..
Overview of the Recognition System
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Department of Computer Science
Proposed Technique for Recognition..
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
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Department of Computer Science
Proposed Technique for Recognition..
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 :
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Department of Computer Science
Proposed Technique for Recognition..
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
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Department of Computer Science
Proposed Technique for Recognition..
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
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Department of Computer Science
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.
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Department of Computer Science
Proposed Technique for Recognition..
4. Classification using Neural Networks
4.1 Backpropagation Neural network Structure
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Department of Computer Science
Proposed Technique for Recognition..
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
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Department of Computer Science
Proposed Technique for Recognition..
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 .
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Department of Computer Science
Proposed Technique for Recognition..
4.4 Training Results
Following figure shows the performance of Training , Validation and Test .
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Department of Computer Science
Proposed Technique for Recognition..
4.4 Training Results
Following figure shows the regression of the training results .
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Department of Computer Science
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
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Department of Computer Science
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%
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Department of Computer Science
Proposed Technique for Recognition..
4.4 Test Result
Confusion Matrix given below shows the results of 175 test images passed to the
system at a time .
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Department of Computer Science
Proposed Technique for Recognition..
4.4 Test Results
Following figure shows the results of regression of 175 banknotes
passed to the system at a time .
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Department of Computer Science
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
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Department of Computer Science
Proposed Technique for Verification..
Overview of the Verification System
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Department of Computer Science
SEM imaging and XRD Analysis of genuine and counterfeit banknotes
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Genuine
Genuine
Counterfeit
Counterfeit
Department of Computer Science
SEM imaging and XRD Analysis of genuine and counterfeit banknotes
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Genuine
Counterfeit
Department of Computer Science
Proposed Technique for Verification..
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 .
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Department of Computer Science
Proposed Technique for Verification..
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
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Department of Computer Science
Proposed Technique for Verification..
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
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Department of Computer Science
Proposed Technique for Verification..
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
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Department of Computer Science
Proposed Technique for Verification..
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
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Department of Computer Science
Proposed Technique for Verification..
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
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Department of Computer Science
Proposed Technique for Verification..
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-.
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Department of Computer Science
Proposed Technique for Verification..
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)
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Department of Computer Science
Proposed Technique for Verification..(a) Confusion matrix of training 5000 banknote (b) Regression diagram of 5000 banknote
with 15 genuine and 5 counterfeit banknotes
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Department of Computer Science
Proposed Technique for Verification..(a) Confusion matrix of training 1000 banknote (b) Regression Diagram of 1000 banknote
with 25 genuine and 10 counterfeit banknotes
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Department of Computer Science
Proposed Technique for Verification..(a) Confusion matrix of training 500 banknote (b) Regression Diagram of 500 banknote
with 15 genuine and 10 counterfeit banknotes
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Department of Computer Science
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%
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RA=69/69*100 = 100%
Department of Computer Science
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 .
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Department of Computer Science
Published/Submitted Papers
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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
Department of Computer Science
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.
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Department of Computer Science
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
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Department of Computer Science
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.
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Department of Computer Science
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/
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