ISSN: 2394-3122 (Online) Impact Factor: 3.471 Volume 3, Issue 7, July 2016
SK International Journal of Multidisciplinary Research Hub
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Research Article / Survey Paper / Case Study Published By: SK Publisher (www.skpublisher.com)
Review on to Evaluate and Propose a Novel Technique to Check Genuineness of the Currency Using Digital Image
Processing
Sunetra Chandel M. Tech Research Scholar Electronics &Communication Engineering)
Himachal Pradesh Technical University Hamirpur, India
Abstract:The growing problem of fake currency is evident as newspapers report that lots of fake currency notes are being seized every day. Fake currency is the imitation currency which is produced without the legal permission of the government.
It’s called as counterfeiting. Producing or using this fake money is also illegal. Counterfeiting is said to be as old as money itself. Counterfeiting has been called as the world’s second oldest profession. The growing advancements in the field of computers, photocopier and scanners have made duplicating currency notes very simple. The common man has no means of differentiating fake currency from the genuine one. This has led to an increase in corruption in our country. Corruption hinders growth of the country. Other means through which counterfeiting effects our society includes reduction in the value of real money, inflation, decrease in the acceptability of paper money, losses, etc. This paper reviews various techniques that can be used to detect fake currency notes. Some of the methods that can be used to detect fake currency are water marking, security thread, water marking, optically variable ink, latent image, counterfeit pen detection etc. MATLAB is used.
Key Words: Counterfeiting, Digital image processing, Feature extraction, Indian currency features.
I. INTRODUCTION
Digital image processing is described as the use of different computer algorithms to perform image processing on digital images [10]. Digital image processing is an area which is characterized by the need for extensive experimental work. Various experiments are required to establish the validity of proposed solutions to the given problems. It is very economical in many fields of research and thus is widely used in various industrial and military applications. DIP is the only practical technology which is used for classification and feature extraction. DIP allows us to use much more complex algorithm. Hence it can offer much sophisticated performances at simple tasks. Digital image processing encompasses the processes whose inputs and outputs are images. Also those that extract attributes from images and include the recognition of individual objects. Currency is a token that is exchanged for purchasing goods or services. Currencies can be in diverse forms. Paper currency is used in different countries for transaction as they are easy to handle, cheap to manufacture and durable [1]. The Indian currency is entirely made up of a blend of cotton, paper and linen. The bark of the tree from which the currency is made is called the Balsam. Balsam contains a sufficiently large percentage of cellulose. It is known that cotton is the purest form of cellulose [6]. The Indian currency system is prevalent since a long time. Dating back to history, the government of India introduced its first paper mo ney, by issuing ten rupees notes in 1861. The RBI (Reserve Bank of India) widely known as central bank of India, began its note production in 1938. It issued 2,5,10,100 and 1000 rupees notes. Currently the Indian currencies have the denomination of rupees 5,10,20,50,100,500 and 1000. Each and every denomination has its value on it [7]. Counterfeiting is the circulation of imitation currency which is produced without the legal permission. Since many years counterfeiting of paper currency has been
Volume 3, Issue 7, July 2016 pg. 12-17 challenging the financial system of various countries including India. Modernization of the financial system is a milestone in protecting the economic prosperity and maintaining social harmony. Automatic machines capable of recognizing banknotes are massively used in automatic banking operations [5]. The measures to prevent counterfeiting have been ongoing since the Roman times. The development of sophisticated printing techniques has led us to the scenario that the counterfeit currencies has become on-par with the original currency. The earliest methods to detect counterfeit money were to use UV detection. It was based on the principle of detection of special kinds of inks that are visible under UV light. Since this process was slow, automation has been introduced [6]. The commonly used methods to detect fake notes are:
A. See through register
The small floral designs on the front (or hollow) and the back (filled up) of the note in the middle of the vertical band which is next to the watermark have an accurate back to back registration. The designs will appear as floral designs when you see it against the light.
B. Water marking
All of the Mahatma Gandhi series banknotes contain the Mahatma Gandhi watermark with a light and shade effect. It also contains multi-directional lines in the watermark window.
C. Optically variable ink
Optically variable ink is a new feature added in rupees 1000 and 500 notes. The revised color scheme was introduced in November in the year 2000. The number 1000 and 500 printed on the obverse of rupees 1000 and 500 is printed in optically variable ink. It means a color shifting ink. The color of the numeral will appear green when the note is held flat but it would change when the note is held at a different angle.
D. Fluorescence
The number panels of the notes are printed in fluorescent ink. The notes also have optical fibres. Both of these can only be seen when the notes are exposed to ultra-violet lamp.
E. Security thread
The notes of rupees 500 and 100 have a security thread with similar visible features and the inscription “Bharat” (in Hindi) and “RBI”. When held against the light, the security thread on rupees 1000, 500 and 100 can be seen as one continuous line. The rupees 5, 10, 20 and 50 notes contain a readable, fully embedded windowed security thread with the inscription of words “Bharat” ( written in Hindi) and “RBI”. The security thread appears to the left of the Mahatma’s portrait.
F. Intaglio printing
The portrait of Mahatma Gandhi, guarantee and promise clause, the RBI seal, Asoka pillar Emblem on the left, RBI governor’s signature are all printed in intaglio. Intaglio means in raised prints. These prints are those which can be felt by touch. It’s in rupees 20, 50, 100, 500 and 1000 notes.
G. Latent image
On the obverse side of rupees 1000, 500, 100, 50 and 20 notes, there is a vertical band. This vertical band is on the right side of Mahatma Gandhi’s portrait. It contains a latent image showing the respective denominational value in numeral.
The latent image is visible only when the note is held horizontally at the level of the eye.
Volume 3, Issue 7, July 2016 pg. 12-17 H. Micro lettering
This is the feature that appears between Mahatma Gandhi’s portrait and the vertical band. It always contains the word
“RBI” in rupees 5 and 10. The notes of rupees 20 and above also contain the denominational value of the notes in micro letters. This feature can be seen well under a magnifying glass.
I. Identification mark
Each and every note has a unique mark of its own. Special features in intaglio have been introduced on the left of the watermark window. This feature is available in different shapes for various denominations (20-vertical rectangle, 50- square, 100-triangle, 500-circle and 1000-diamond). It also helps the visually impaired to identify the denomination [9]
[11].
II. RELATED WORK
Vishnu R. et al. [1] proposed that currency recognition was one of the fastest growing research fields under image processing. This paper proposed a novel method for Indian currency recognition. The main objective of this scheme was to identify features like Numeral, Shape, colours etc. Principal Component Analysis was used to reduce the dimensions and a similarity based classifier was constructed to predict test sample. Results were also validated by constructing models using classifier implemented using WEKA and testing with unseen samples not considered in feature extraction. This study demonstrated that center numeral results in an accuracy of 100% with all family of currencies.
Santhanam K. et al. [2] proposed that the growing menace of fake and counterfeit currency was evident as newspapers reported of a huge cache of fake currency notes being seized appeared every day. In Digitized currency they provide countermeasure to identify wrong currency. It encompasses polarization concept, image processing technique and holographic detect method to detect fake currency on the physical properties of the currency rather than existing methodology of its chemical properties. This scheme was achieved by mathematical label, experimental verification and lab view simulation and also through automation to enhance reliability and accuracy in a cheaper and efficient manner.
Vipin K. et al. [3] proposed that it was very difficult to count the different types of notes in a bunch. This paper emphasized an image processing technique to extract paper currency. The extracted ROI can be used with neural networks and pattern matching techniques. They have used different pixel level for the denomination of notes. The pattern recognition and neural network matcher technique is used to match the currency of the paper notes.
Binod Y. et al. [4] proposed that counterfeit notes are one of the biggest problems in cash transactions. It had become one of the biggest hurdles for INDIA. Due to easy printing, it was very easy to print fake notes within seconds using latest tools.
Detecting fakes notes manually was time consuming and vary untidy. So there was a need of a machine that automatically detects fake currency in an efficient manner. Many techniques have been proposed with the use of MATLAB, feature extraction with HSV color space and other applications of image processing.
Deborah M. et al. [5] proposed that fake currency detection was a process of finding the forgery currency. After choosing the image, pre-processing is applied. In pre-processing, the image was to be cropped, smoothened and adjusted. Convert the image into gray color. After conversion apply image segmentation. The features are extracted and reduced. Finally compare the image into original or forgery.
Alekhya D. et al. proposed that counterfeit money was imitation currency produced without the legal sanction of the state or government. Producing or using this fake currency was a form of fraud or forgery. Counterfeiting was as old as money itself, and was sufficiently prevalent throughout history that it had been called “the world’s second oldest profession.” This had led to increase of corruption in our country hindering country’s growth. Some of the effects that counterfeit money had on society include a reduction in the value of real money and inflation due to more money getting circulated in the society. Some of the
Volume 3, Issue 7, July 2016 pg. 12-17 methods to detect fake currency are watermarking, optically variable ink, security thread, latent image, techniques like counterfeit detection pen using MATLAB.
III. TECHNIQUES OF FEATURE EXTRACTION IN IMAGE PROCESSING
In image processing, feature extraction can be described as the special form of dimensionality reduction. It’s the method of capturing the visual content of images for indexing and retrieval. When the input data to an algorithm is too large to be processed and it’s suspected to be redundant (much data but not much information) then the input data would be transformed into a reduced representation set of features (also named as feature vector). If the attributes extracted are carefully chose n, it’s expected that the attribute set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of full size input. Feature extraction involves simplifying the amount of resources required to describe the large set of data [5].The main techniques of feature extractions are:
A. Sobel Edge Detection: the Sobel operator performs a two dimensional spatial gradient measurement on an image and so emphasizes regions of high spatial frequency that correspond to edges. Typically it is used to find the approximate absolute gradient magnitude at each point in an input gray scale image. A pixel is said to be at an edge when intensity sharply changes to its neighbours. The edge itself is a linear shape along which the change is maximal. Edges are detected of the gray scale image of paper currency using Sobel operator [5].
B. Canny Edge Detection: The purpose of edge detection in common is to significantly trim down the amount of data while preserving the structural properties to be used for further image processing in a particular image. The canny edge detection algorithm runs basically in five separate steps.
Smoothening: in this step, blurring of image is done to remove the noise.
Finding gradients: the edges should be marked where the gradients of image has large magnitudes.
Non-maximum suppression: only local maxima should be marked as edges.
Double thresholding: potential edges are determined by thresholding.
Edge tracking by hysteresis: final edges are determined by suppressing all edges that are not connected to a very certain (strong edge).
The canny algorithm basically finds edges, those where the intensity of the gray scale image changes the most. Then these areas are found by determining the gradients of the image. The Gradients at each pixel in the smoothed image are determined by applying Sobel operator.
C. Hough Transformation: Hough transform is a method for estimating the parameters of a shape from its boundary points.
This idea can be generally be called as to estimate “parameters” of arbitrary shapes. The Hough transform can be used to detect lines, circles or other parametric curves. Hough transform was introduced in 1962 and was first used to find lines in images a decade later. The goal is to find location of lines in images. Hough transform can detect lines, circles and other structures if their parametric equation is known. It can give robust detection under noise. Borders between the regions are straight lines. These lines separate regions with different gray levels. Edge detection is often used as pre-processing to Hough transform. The input image must be a threshold edge image. The magnitude results computed by the Sobel operator can be threshold and used as input.
The flow diagram below shows the step by step process of this paper currency verification system. The first stage is the image acquisition stage in which we obtain the image because without an image no processing is possible. The image acquired is in RGB color. It is converted into gray scale because it contains only the intensity information which is easy to process instead of processing three components. Edge detection is the name for a set of mathematical models which aim at
Volume 3, Issue 7, July 2016 pg. 12-17 identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
The points at which image brightness changes sharply are typically organized into a set of curved line segments termed as edges. Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or to change the representation of an image into something that is more meaningful and easier to analyze.
It’s used to locate objects and boundaries in images. Feature extraction is a special form of dimensionality reduction.
Transforming the input data into the set of features is called feature extraction [10].
Figure1: Flow diagram showing algorithm
IV. CONCLUSION
In this paper principal features of Indian currency were reviewed. Knowing these features there are many methods for identifying a fake note. Each one has its own significance. One should be cautious while detecting a fake note. the authentication of Indian paper currency is described by applying image processing techniques. The process begins from image acquisition and ends at comparison of features.
References
1. J. Qian , D. Qian and M. Zhang, ″A Digit Recognition System for Paper Currency Identification Based on Virtual Instruments,” IEEE, 2006.
2. F.H. Kong, J. Q. Ma and J. F. Liu, “Paper Currency Recognition Using Gaussian Mixture Models based on Structural Risk Minimization,” Fifth International Conference on Machine Learning and Cybernetics, Aug. 2006.
3. N. Jahangir and A. R. Chowdhury, “Bangladeshi Banknote Recognition by Neural Network with Axis Symmetrical Masks,” IEEE, 2007.
4. H. Hassanpouri, A. Yaseri and G. Ardeshiri, “Feature Extraction for Paper Currency Recognition” 1-4244-0779-6/07/2007 IEEE.
5. R. Mirza and V. Nanda, “Design and Implementation of Indian Paper Currency Authentication System Based on Feature Extraction by Edge Based Segmentation Using Sobel Operator,” vol. 3, Issue 2, pp. 41-46, Aug. 2012.
6. K. Santhanam, S. Sekaran, S. Vaikundam and A.M. Kumarasamy, "Counterfeit Currency Detection Technique Using Image Processing, Polarization Principle and Holographic Technique," Fifth International Conference on Computational Intelligence, Modelling and Simulation (CIMSim), pp.231-235, Sept. 2013.
7. V.K. Jain and R. Vijay, “Indian Currency Denomination Identification Using Image Processing Technique,” International Journal of Computer Science and Information Technologies,vol. 4, pp. 126 - 128, 2013.
8. Vishnu R and B. Omman, “Principal Features for Indian Currency Recognition,” Annual IEEE India Conference (INDICON), 2014.
Volume 3, Issue 7, July 2016 pg. 12-17
9. B. P. Yadav, C. S. Patil, R. R. Karhe and P.H Patil, “An automatic recognition of fake Indian paper currency note using MATLAB,” International Journal of Engineering Science and Innovative Technology, vol. 3, Issue 4, July 2014.
10. M.Deborah and C.S. Prathap, “Detection of Fake currency using Image Processing,”I nternational Journal of Innovative Science, Engineering &
Technology, vol. 1, Issue 10, December, 2014.
11. D. Alekhya, G. D. S. Prabha and G. V. D Rao, “Fake Currency Detection Using Image Processing and Other Standard Methods,” International Journal of Research in Computer and Communication Technology, vol. 3, Issue 1, January- 2014.
AUTHOR(S)PROFILE
Sunetra Chandel, received B.Tech in Electronics & Communication Engineering from Himachal Pradesh University, Shimla in 2013. She is currently an M. Tech Candidate in the department of Electronics & Communication Engineering at the Himachal Pradesh Technical University, Hamirpur. Her current research interest includes a Review on to evaluate and propose a novel technique to check genuineness of the currency.