The Term “Fake Currency” refers to the currencies which are duplicated or forged in nature. Hence it is important to detect them in real-time. Currencies are the greatest exchanging system so far used in most of the countries. Cash depository machines in the banks and the cash counting machines in the banks help us identify the forged currencies presented.

“We presenting you the article which is contented with indispensable stuff on fake currency detection using image processing Matlab”

This detection involves the function called image processing. It is the process of enriching the images and retrieving the necessary details in them. In other words, conversion of the image into a digital format is known as image processing. Modifications in the images presented are handled by customized algorithms.

Matlab is one of the best image processing tools used by the top experts in the fields of fake currency detection. Besides, it offers the top algorithm models and frameworks for analyzing the images. At the end of this article, you will be educated with all the possible aspects comprised in the fake currency detection using image processing Matlab. Now we can see the basic Matlab functions for fake currency detection.

Matlab Functions for Fake Currency Detection

• Color Map Selection ()
  • Image Observation under Different Filters
• Range Display ()
  • Image Range Display
• Image Cropping ()
  •  Section wise Image Cropping
• Contrast Fine Tuning ()
  • Image Contrast Adjustments
• Image Detail Exhibition ()
  • Image File & Image Info
• Distance Measurement ()
  • Pixel Distance Dimension
• Pixel Sectioning ()
  • Increased Pixel Views
• Pixel Detail Exhibition ()
  • Pixel Info by Cursor Pointing

The above listed are some of the interesting functions offered by Matlab in the fake currency detections. However, some limitations exist in the imitated currency detection. You all get curious regarding the limitations involved in the fake currency findings. Here we have mentioned the same for the ease of your understanding. Shall we get into that phase? Here we go!

Limitations of Fake Currency Detection

• Distance Image Retrieval (Extreme)
  • Terminating excess portions (core points) in the images leads to accuracy mismatch (descriptors)
• Distance Image Retrieval (Nearby)
  • Terminating excess portions (core points) in the images leads to tough precision match (descriptors)
• Numerous Folding / Crumpling
  • It leads to discrepancy indescriptors by altering the core points

The above listed are some of the limitations pampered in the fraud currency detections. However, we can overcome these constraints by using the Matlab tools for image processing.   Currency is also termed as coins, bills, cash and The above listed are some of the limitations pampered in the fraud currency detections. However, we can overcome these constraints by using Matlab tools for image processing. Currency is also termed as coins, bills, cash, and exchange, etc., in the subsequent passage, our fake currency detecting technical team presented you with the crucial methods involved in the coin detections.

Coin (Currency) Detection Methods

• Registration of Images Method
• Eigen Space Decomposition Method
• Vector Quantization Method
• Statistical Method

In the aforementioned passage, we mentioned to you the methods to discover the coins. Our researchers are experts in these areas by experimenting with many more algorithms and techniques in fake currency detection areas they deliberately know the edges concreted in it. In addition to these statements, our technical team wanted to list out the features filled in the fake currency detection to make you much wiser. Are you ready to feed them up? Come let’s have the interesting facts.

What are the Features for Fake Currency Detection?

• Infrared Sensor
  • Infrared (IR) Spectrum
  • Numerous IR Featured Sensors
• Security Thread
  • Pulsed Eddy Method
  • Electromagnetic Recognition
• Fluorescence Feature
  • X Rays
  • Pattern UV
• Serial Number Printing
  • Exact Tie Points
  • Radial-based Function-RBF
  • Edge & Binary Finding
• Illumination
  • Brightened Y Histogram (YIQ)

The foregoing passage has conveyed to you the features and the methods utilized in the fake currency detection approaches. We hope that you are getting our perceptions and insights. On the other hand, fake currency detection can be identified with the several steps of image processing. Yes, the next phase is all about the steps involved in fake currency recognition. Let us try to understand them.

Fake Currency Recognition using Image Processing

• Scanning & Reading of Images
  • Scanning & JPEG Format Image Storing
  • Unrecovered Compression Loss
  • Matlab Image Reading even without Key Points
• Smoothing of Images
  • Removal of Blurs in Filtering
• Binary & Gray Scale Transfiguration
  • Binary- Image Partitions & Contrast
  • Gray- Color Shades
• Segmentation of Images
  • Fragment Prearrangements
  • Pixel Comparisons(Shades & Contrast)
  • Edge Variations Analysis
• Extraction of Features
  • Currency Structure & Classifier
  • Feature Observation (Size & Texture)
• Recognition of Patterns
  • Forgery Detections (legitimate or not)

In the previous passage, our experts have bulletined you the key points involved in the recognition process (fake currency). These steps need several toolboxes to implement their process accurately. Don’t squeeze your head regarding the toolboxes. Our technical team also listed you the various toolboxes used in fake currency detection. We know that you are getting curious about these areas. Let us have a quick insight!!

Matlab Toolboxes for Fake Currency Detection

• Curve Fitting Toolbox
• Machine Learning & Statistical Toolbox
• Optimization Toolbox
• Fuzzy Logic Toolbox
• Data Procurement Toolbox
• Computer Vision Toolbox
• Deep Learning Toolbox
• Image Processing Toolbox
• Image Retrieval Toolbox

The listed above are the toolboxes used to detect the forged currencies. Finding the fake currencies is the hectic process when it is comes to manual processes actually.  To eradicate these constraints we can make use of the toolboxes and functions developed to identify the forged currencies.  In fact, our experts are well versed in the toolboxes and the functions of the same. Here, we are going to demonstrate you the latest functions of the fake currency detections.

Latest Functions for Fake Currency Detection

Image Acquisition Functions

• Custom Input Creation
  • Imaqreset: Elimination of acquired features
  • Videoinput: Video Input creation
  • Iamghwinfo: Image procurement device details
  • Save:  MAT file
  • Load: Workspace of Matlab
  • Imagmontage:  Montage image frames
  • Obj2mfile: Matlab object conversion (Video)
  • Flushdata: Data removal
  • Delete: Object deletions
  • Clear: Matlab object clearances
  • Stop: Discontinuity of input
  • Wait: Staying in process
  • Isvalid: Resemblance with devices
  • Islogging: Logging acknowledgement
  • Start: Device execution
  • Set: Object configuration
  • Getasnapshot: Solitary object frames
  • Peekdata: Recent objects
  • Getdata: Object acquirement
  • Imag.VideoDevice: Video frames

Type Conversion Function

• Data Types Conversion
  • Im2uint8: Unsigned 8 bits integer conversion
  • Im2uint16: Unsigned 16 bits integer conversion
  • Im2single: Image accuracy conversion
  • Im2int16: Signed 16 bits integer conversion
• Conversion of Image Types
  • Imsplit: Multichannel to individual channel
  • Demosaic: Color conversion ( Bayer Pattern)
  • Label2rgb: Label to RGB conversion
  • Mat2gray: Matrix to gray scale conversion
  • Ind2gray: Index to gray scale conversion
  • Gray2ind: Binary/ gray scale to index conversion
  • RGB2lightness: RGB to lightness conversion
• Quantization Indexed Image Conversion
  • Grayslice: Gray scale image to index
  • Imquantize: Output values / levels
  • Multithresh: Otsu’s Multi-tier image threshold
• Thresholding Binary Image Conversion
  • Graythresh: Otsu’s image threshold
  • Otsuthresh: Otsu’s histogram threshold
  • Adaptthresh: Local first-order stat lenient image threshold
  • Imbinarize: 2D/3D image threshold

Batch Processing & Image Sequences Function

• Conversion of Color
  • Applycform: Color transformation application
  • Makecform: Color transformation creation
• Conversion of Color Space
  • Colorcloud: 3D gamut display
  • Ycbcr2rgbwide: YCBCR color to RGB color (Gamut)
  • Ycbcr2rgb: YCBCR color to RGB color
  • Xyz2rgbwide: CIE 1931 xyz to RGB color (Gamut)
  • Xyz2rgb: CIE 1931 xyz to RGB color
  • Xyz2lab: CIE 1931 XYZ to 1976 l*a*b*
  • Ntsc2rgb: NTSC to RGB
  • Lab2xyz: 1976 l*a*b* to CIE 1931 XYZ
  • Rgbwide2ycbcr: RGB to CIE 1931 XYZ(Gamut)
  • Rgb2ycbcr: RGB to YCBCR
  • Rgb2xyz: RGB to 1931 XYZ CIE
  • Rgb2ntsc: RGB to NTSC
  • Rgb2lab: RGB to 1976 L*a*b* CIE
  • Lab2rgb: 1976 L*a*b* CIE to RGB
• Conversion of Color Values from Data Type
  • Lab2uint8: L*a*b to uint8
  • Xyz2uint16: XYZ to uint16
  • Xyz2double: XYZ to double
  • Lab2uint16: L*a*b to uint16
  • Lab2double: L*a*b* to double
• Color Variance Measurement
  • DeltaE: Standard CIE76
  • Imcolordiff: Standard CIE2000 / CIE94
  • Colorangle: Vectors of RGB
• Images of Synthetic
  • Phantom: Head phantom creation
  • Imnoise: Noise adding
  • Checkerboard: Checkerboard creation
• Automated White Balancing
  • Whitepoint: Illuminants XYZ color values
  • Rgb2lin: RGB values (Linearize gamma free)
  • Lin2rgb: Linear RGB gamma correction
  • Illumpca: Analysis of principal component
  • Illumgray: Gray world(Algorithm)
  • Chromadapt: RGB color balancing adaptation
  • Illumwhite: White patch retinex (Algorithm)

So far, we have listed you the entire functions involved in the latest fake currency detections. We hope that you are getting the point as of now that is explained. Our experts also wanted to mention the datasets used in the same approach for your better understanding. Shall we get into that phase? Of course yes, come let’s we have one of the important sections.

Datasets for Fake Currency Detection

• Study Case of Egyptian Banknotes (ICTA2015)
• Image Database for Multi-national Banknotes

Let’s have their descriptions in the immediate phases.

Study Case of Egyptian Banknotes (ICTA2015)

• Examination of sample images for every classes (120/20)
• Scanned copies of currency notes (200,100,50,20,10 and 5)

Image Database for Multi-national Banknotes

• Currency & No.of Images
  • USD: 45574
  • RUB: 12146
  • KRW: 536
  • JPY: 1462
  • EUR: 4324
  • CNY: 626