Fake Currency Detection Using ORB Algorithm

International Journal of Advance Research, Ideas and Innovations in Technology, 2019

Around 150+ currencies exist in the world. Each currency differs from the other on the basis of size, paper, colors, patterns, text, etc. It is difficult to identify all the currencies that exist. Also, it is difficult to determine whether a currency is real or fake. Our system proposes to tackle this problem using Image Processing in MATLAB. The different types of currencies from the different origin are provided to the system and system then performs Image Processing operations depending on the currencies and provide the identified currency type as an output. Also to authenticate currency is real or fake it performs Image Processing functions and identifies the currency provided as an input is real or fake.

IRJET, 2020

Fake Indian Currency Note (FICN) is a term used by officials and media to refer to counterfeit currency notes circulated in the Indian economy. However several central and state agencies are working together and the Ministry of Home Affairs has constituted the Fake Indian Currency Notes Coordination Centre (FCORD) to curb this menace. We propose a new approach to detect fake Indian notes using mobile camera and Image processing. We will convert Indian currency into more the 150+ different currencies values to help tourist all over the world. In this project we match features between two images using Brute-Force Matching with ORB Descriptors. After applying BFMatcher with ORB we get Matcher Object and by using knnMatch we get the list of best k matches depending upon the value of k.

2021

Malpractising has always been a serious challenge that resulted in a serious problem in society. The automation in technology creates a more copied currency that is entirely spread, resulting in reducing the economic growth of the country. The note detection is compulsory, and also necessary to be very consistent and reliable. The paper currency identification depends upon a number of steps, including edge detection, feature extraction, image segmentation, grayscale conversion, and comparison of images. This paper also consists of a literature survey consisting of different methodologies for detection. The review to detect malpractice concludes that whenever we apply some efficient preprocessing and feature extraction techniques, it helps in improving the algorithm as well as the detection system. Machine Learning techniques help in building tools that is required and necessary for the research work, and we can make computer learning design, implementation, and methods to have a dif...

IRJET, 2022

The global economy is vulnerable to counterfeit currency. Advanced printing and scanning technologies have made it a common occurrence. For both people and corporations, fake currency recognition is a serious issue. The creation of counterfeit banknotes, which are barely distinguishable from legitimate currency, is a continuous process for counterfeiters. To detect fake notes, several traditional techniques and approaches are available based on colors, widths, and serial numbers. This paper discusses different methods of fake currency detection using image processing.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024

This project aims to develop a robust fake currency detection system leveraging image processing and machine learning techniques. Data cleaning involves image quality enhancement and handling of torn or dirty notes. The experimental setup includes a digital camera in a controlled lighting environment to capture currency images. MATLAB is used for software setup due to its extensive libraries.

IRJET, 2020

This paper developed a computer vision based approach for Indian paper currency detection. In this approach, extract currency feature and develop an own datasets used for the currency detection. By using feature extraction method of front and back side Rs. 200 denomination security feature of Indian currency note. The mainly use ORB (Oriented FAST and Rotated BRIEF) and Brute-Force matcher approach to extract the feature of paper currency, so that can more accurately detection the denomination of the banknote both obverse and reverse. Our main contribution is through using ORB and BF matcher in OpenCV based, the average accuracy of detection is up to 95.0% and tested this method on different denominations of Indian banknote.

International Journal of Trend in Scientific Research and Development, 2018

Nowadays problem of fake currency increases because of increasing in technology like scanning, color printing so result in counterfeit currency. In India increase in fake paper currency notes of 100, 500, 2000 rupees etc. So detection of fake currency is n The determination of fake currency with the help of image processing. Firstly Image acquisition is done then pre-processing stage applied to that image for suppress unwanted feature and added some feature which are necessary for further process. Conversion of RGB picture into HSV scale. Then image segmentation applied to that image in this image divided into number of objects. Then morphological operation is perform on that picture. Further feature extraction/area of calculation stage applied to that picture and finally that picture compared with the original image.

2021

Student Dept. of Comp. Engineering, Pillai College of Engineering, New Panvel, Maharashtra, India -----------------------------------------------------------------------***-----------------------------------------------------------------------Abstract— Fake currency is the money produced without the approval of the government, creation of it is considered as a great Offence. The elevation of colour printing technology has increased the rate of fake currency note printing on a very large scale. Years before, the printing could be done in a print house, but now anyone can print a currency note with maximum accuracy using a simple laser printer. This results in the issue of fake notes instead of the genuine ones has been increased very largely. It is the biggest problem faced by many countries including India. Though Banks and other large organizations have installed Automatic machines to detect fake currency notes, it is really difficult for an average person to distinguish between th...

— The issue of efficiently distinguishing counterfeit banknotes from genuine ones via automatic machines has become more and more important. This proposed system describes an approach for verification of Indian currency banknotes. Fake Indian currency of 100, 500 and 1000 rupees seems to have flooded the whole system and there is no proper way to deal with them for a common person. There is a need to design a system that is helpful in recognition of paper currency notes with fast speed and in less time. The recognition system is composed of two parts. The first is preprocessing, including detecting edges, compressing data dimensionalities, and extracting features. The second one is comparison with the pre stored database. The result is displayed by the glow of led. In order to make system complete, we need to maintain a database for storing the characteristics of the currencies.The result will be whether currency is genuine or counterfeit and the efficiency of our system is approximately 95% which can be further increased using more advanced techniques and researches. Furthermore, some related applications and the suggestions of the further work are discussed.

International Journal of Research, 2015

Two qualities of Indian paper money are chosen for fake discovery included recognizable proof imprint and coin serial number. The attributes extraction is performed on the picture of the coin and it is contrasted and the qualities of the certified money. The money will be checked by utilizing picture preparing methods. The procedure of shrewd edge discovery calculation is utilized for edge recognition. The methodology comprises of picture handling, dim scale change, edge recognition, picture division, trademark extraction, looking at pictures. Keywords: Fake currency; Canny Operator; Digital image processing; imitation detection.