Fake currency in the sense, currencies that were manipulated in other ways (duplicated). Identification of the fake currencies can be held with the help of the Matlab tool. 

As we are living in the global we tend to face different currencies of various countries. As we are not aware of the other countries’ currencies it is difficult to detect fake currencies.  Hence it is important to implement a system in which images can process automatically. Algorithms are pillaring the process by their significant features like segmentation, pattern recognition, RGB conversion, image binaries, and elimination of image noises.

“In this article, we presented you the significant features comprised in the Matlab to detecting fake currency”

Generally, currencies are playing a vital role in the economy as well as in the day to day life. We cannot imagine the world without currencies. Some fraudulent guys in the world are trying to imitate the original currencies by performing illegal actions. In the subsequent passage, we have enumerated the overview of fake currency detection for your better understanding. Let’s start with the basic aspects involved in it.

Overview of Fake Currency Detection

  • Currencies from all over the world definitely vary from each other
  • For example the variance may be in the form color, texture, size, pattern and symbols
  • Bankers highly benefited by the accurate currency identification system
  • Fake currency detection is possible by the Matlab image processing
  • Image pixels are enriched by the Matlab image processing
  • Used techniques are pattern and texture classification etc.,

This is the short and sweetest overview of the fake currency detection system by the image processing using the Matlab tool. You might get a question here like why do we need to use Matlab for the fake currency detections. The answer is given to you in the subsequent passage to the ease of your understanding.

Research Assistance to implement matlab to detecting Fake Currency

Currency Detection using Matlab

  • Visualization Toolboxes (2D/3D)
  • In-built Functions
  • Enhanced Algorithms
  • Data Segmentation
  • Image Comparisons
  • Massive Dataset Batch-process
  • Histograms and Regions of Interest
  • Image Enrichment
  • Elimination of Noise
  • Geometric Transforms
  • Registration of Images

The above listed are the significant reason that is behind the Matlab to Detecting Fake Currency. Our researchers are very familiar with the Matlab tool. By conducting researches in the Matlab tool they deliberately know every process pampered in the fake currency detection. 

Generally, students need guidance while doing Matlab to detect fake currency projects. There is nothing wrong with it. Every student from all over the world is getting our assistance in their project and research areas. Now we can have the next section called features for the fake currency detection.

Features for Matlab to Detecting Fake Currency

Internal Features of Currency Detection

  • Currency Color
  • Currency Symbols
  • Currency Serials

External Features of Currency Detection

  • Physical Features
  • Currency Size
  • Currency Width

These are the main features of every currency presented in the world. However, both are not reliable. Currencies are circulated through various hands of the people in the world. Hence, they are subject to damages and variations. So that recognition of the fake currencies will get challenges in these cases. On the other hand, the extraction of currency features consisted of some of the aspects. Yes, we are going to list the currency extracting features for ease of your understanding. 

Currency Features Extraction for Detection

The above listed are the essential features involved in currency extraction. These features are to be processed under several processes called preprocess, mid-process, and post-process. Let’s have their explanations in the upcoming passage. 

Our researchers in the institute always focus on the students’ welfare. In the event of this object, they framed this article with utmost care. Our technical team is also rendering project assistance to the students and scholars with effective demonstrations. Students are highly benefited by our perceptions and ideas by adding into their Matlab to detecting fake currency projects. You may think that is there no techniques to detect fake currencies. Yes, of course, there are some techniques to detect fake currencies. We have itemized the techniques for the ease of your understanding. 

Techniques for Currency Detection
  • Pre-processing Images
  • Detecting Edges
  • Recognizing Patterns
  • Matching Templates
  • Classification

The aforementioned are the techniques that get involved in fake currency detection. Now let us see the process flow of fake currency recognition.

Process Flow of Currency Recognition

  • Pre Processes of Currency Recognition
    • Conversion of RGB to Gray
    • Image Augmentation & Compression
  • Mid Processes of Currency Recognition
    • Segmentation of Template
    • Detection & Clustering of Features
    • Volume Recognition of Features
  • Post Processes of Currency Recognition
    • Decision Making
    • Validation

The listed above are the 3 processes involved in the fake currency recognition flow. We hope that you would have understood the above-stated areas. If you do need any clarifications you can approach our experts at any time. 

As this article is titled with the Matlab to detecting fake currency, here we are going to demonstrate to you how to build the Matlab model for currency detection. Let us try to understand the concept by the number of steps.

How to Build the Matlab Model for Currency Detection?

  • Step 1: API Image Processing
  • Step 2: Currencies Scanning
  • Step 3: Resolution Configuration (600 Dots Per Inch)
  • Step 4: Higher/Lower Resolution Performance
  • Step 5: Preprocessing Pixel (21× 29.7cm)
  • Step 6: Mid Processing Pixel (7016 × 4961)
  • Step 7: Post Processing Pixel (1024×768)
  • Step 8: According to the tasks, post process the inputs

The listed above are the steps involved in the Matlab currency processing. Matlab’s API performs efficient image processing which consumes the least time for it. After the image processing, the currencies were scanned through the scanner. The third step is to attain the scan accuracy by the DPI. The acronym stands for the Dots per Inch. Performance of the DPI is based on the higher or lower resolution. In the end, the image gets processed under 3 pixels as mentioned above.

At the same time, it is important to store the currency features in the database with the Graphical User Interface (GUI). For this, a complete system with the Matlab aspects needs to be programmed. The system is capable of handling numerous processes. Let’s have further explanations in the following areas.

Working Procedure for Currency Recognition

  • JPEG Image Reading
  • Image Preprocessing
  • Noise Elimination
  • Image Smoothening
  • Detecting Edges
  • Image Segmentation
  • Matching Patterns

TThis is how Matlab processes the images in real-time. Matlab’s scope doesn’t lie in these areas. As well as, they are highly capable of managing numerous functions as mentioned in the upcoming areas. Yes, our researchers have also listed you the plenty of functions that are done by the Matlab to Detecting fake currency to make you much wiser. Are you ready to feed them up into your brain? We know that you are very curious about this section. This is an important section so try to give your concentration!!!

List of Matlab Functions for Fake Currency Detection

Matlab Color Conversion Functions

  • ycbcr2rgb (): Ycbcr to RGB
  • rgb2ycbcr (): RGB to Ycbcr
  • rgb2lightness (): RGB to lightness
  • rgb2lab (): RGB to 1976 l*a*b* CIE
  • rgb2hsv (): RGB to HSV
  • rgb2gray (): RGB to gray scale/ color map
  • label2rgb (): Label matrix to RGB
  • label2idx (): Label matrix to linear index
  • lab2rgb (): 1976 L*a*b* CIE to RGB
  • iptcheckmap (): Color map validation
  • integralImage (): Integral 2D Image
  • integralBoxFilter (): Filtered Integral 2D Image
  • ind2rgb (): Indexed to RGB images

Matlab Transformation Functions

  • watershed (): Transform watershed
  • psnr (): Peak signal-to-noise ratio
  • projective2d (): Geometric 2D projections
  • imwarp (): Geometric image transformation
  • imregcorr (): 2D correlated geometric image
  • imhmin (): (H) minima
  • imhmax (): (H) maxima
  • imextendedmin (): Extended minima
  • imextendedmax (): Extended maxima
  • hough ():Transform hough
  • fitgeotrans (): Pair geometric control
  • bwdist (): Distanced image binary
  • affine3d (): Affine 3D
  • affine2d (): Affine 2D
  • imfindcircles ():  Hough circles
  • houghpeaks(): Hough peaks finding
  • ifft2 (): Fast Fourier transform (2D)

Matlab Image Segmentation Functions

  • superpixels (): 2D image segmentation
  • Imsplit (): Multichannel to individual channel images
  • Houghlines (): Line segment extraction
  • Boundarymask (): Boundaries of region
  • Bwboundaries (): Binary boundaries tracing
  • grayconnected (): Contiguous gray image
  • imfill (): Image holes
  • imregionalmin (): Minima
  • imregionalmax (): Maxima
  • inpaintcoherent (): Coherent image restoring
  • regionprops (): Regions properties
  • bwareaopen (): Small object removals

Matlab Image Preprocessing Functions

  • Stretchlim (): Stretch image contrast limits
  • Strel (): Morphological component
  • Padarray (): Array of pads
  • Otsuthresh (): Otsu’s histogram threshold
  • ordfilt2 (): 2D filtering
  • offsetstrel (): Morphological offset component
  • Multithresh (): Otsu’s multi-tier threshold
  • medfilt2 (): Filtering median (2D)
  • mean2 (): Mean component
  • iptcheckconn (): Argument validation check
  • intlut (): Lookup integer
  • integralImage (): 2D images
  • integralBoxFilter (): Box filtering 2D
  • imtranslate (): Image translation
  • imtophat (): Filtering top hat
  • imrotate (): Image rotation
  • imresize (): Image resizing
  • imref3d (): 3D image references
  • imref2d (): 2D image references
  • imreconstruct (): Reconstructing by morphology
  • imread (): Graphic image reading
  • Imquantize (): Output values & levels of quantization
  • Impyramid (): Pyramid compression & extension
  • Imoverlay (): 2D binary mask
  • Imopen (): Open images
  • Immse (): Mean squared error
  • Imlincomb (): Combine linear images
  • Imhist (): Histogram image
  • Imgradientxyz (): 3D gradient finding
  • imgradient3 (): 3D gradient magnitude finding
  • imgaussfilt (): Gaussian 2D filtered images
  • imgaborfilt (): 2D Gabor filtered images
  • imfilter (): Multidimensional ND filtering
  • imerode (): Image erode
  • imdilate (): Image dilate
  • imcrop (): Image crop
  • imcomplement (): Image complement
  • imclose (): Closed image
  • imclearborder (): Border compressions
  • imboxfilt (): Box filtered 2D images
  • imbothat (): Filtering bottom hats
  • Imbinarize (): 2D/3D binary images
  • Imbilatfilt (): Gaussian kernels bilateral filtering
  • Imadjust (): Intensity adjusting
  • Imabsdiff (): Image differences (2)
  • im2uint16 (): 16 bits unsigned integers
  • im2single (): Image to solitary accuracy
  • im2int16 (): 16 bits signed integers
  • im2double (): Image to dual precision
  • ifftshift (): 0 frequency shift
  • histeq (): Histogram contrast equalization
  • getrangefromclass (): Ranged image classes
  • fspecial3 (): Filter 3D
  • fspecial (): Filter 2D
  • freqspace (): Frequency spacing /frequency response
  • fftshift (): Spectrum shift zero-frequency
  • demosaic (): Bayer to color conversion
  • conndef (): Connectivity (array) creation
  • bwunpack (): Binary unpack image
  • bwtraceboundary (): Binary object tracing
  • bwselect (): Binary object selection
  • bwperim (): Binary object perimeter
  • bwpack (): Binary image pack
  • bwmorph (): Binary images morphological function
  • bwlookup (): Lookup tables (nonlinear filtering)
  • bwlabel (): 2D labeled binary images
  • bweuler (): Binary euler images
  • bwconncomp (): Binary image connected components
  • adaptthresh (): Adaptive image threshold

The foregoing passage have conveyed you the crucial functions that are getting done by the Matlab. At the same time, The foregoing passage has conveyed to you the crucial functions that are getting done by Matlab. At the same time, we need to consider the performance metrics determining fake currency detection. There are several terms involved in the metrics and they do play the dominant role in defining the performances. Let’s have a quick insight!

Source Code to Implement Matlab Code to Detecting Fake Currency

Performance Metrics for Fake Currency Detection

  • Receiver Operating Characteristic Curve
  • Decision Thresholds
  • F1- Score & Accuracy
  • Recall & Precision
  • Confusion Matrix

Usually, confusion matrixes are used to solve the dual classification problems. The term precision refers to the proportion between the true positive values assumed by the system. Besides, low precision refers to the precision between the false positive values assumed by the system. 

Recall signifies the dataset’s true positive values and low recall signifies the vice versa (false negative). We can obtain two outcomes by the binary classifications. One is genuine (correct) and another is fabricated results (false).

 For instance, supervised learning is a technique that can be useful for solving binary classification problems. In supervised learning, we fit the samples for the training to attain accurate results. For example, we take an image and we need to know that the image displays a river or pond. Supervised learning compares the test values with the true values to provide the exact data.

So far, we have discussed the needy concepts of Matlab to detecting fake currency. We hope this content can help you to do your projects in this area. If you are facing any difficulties in the projects or researches you can undoubtedly approach our technical team. In a matter of fact, we are always delighted to transfer our knowledge to you!!!