Matlab Code for Fake Currency Detection

The term fake currency refers to the imitated or forged (duplicated) currencies. Feature extraction helps to discriminate the fake currencies among the real currencies presented in the world. Currency recognition system detects the forged currency notes from the genuine ones.

“This is the article which is fully consisted of MATLAB code for fake currency detection with real time concepts in a crystal clear way of explanations”

It is very difficult for the humans to identify the fake currencies presented in all over the world. In fact, it is a hectic task to the human beings. So that it is highly important to integrate a system in which images get processed remotely. Identification of the fake currencies can be held with the help of Matlab tool. This supports the system by performing several tasks such as image preprocessing, segmentation, edge detections and image noise removals & so on. In image preprocessing pixel enrichments and retrieving necessary objects are majorly happened.

At the end of this article, you are going to harvest the important and essential facts oriented with the fake currency detections. In the immediate passage, we have deliberately listed you the benefits of using MATLAB tool in fake currency detection for your better understanding.  Are you interested in learning the concepts of fake currency system, currency recognition? Come let’s we start with the basic aspects involved in it.

Advantages of MATLAB

• Less Hardware Deployments
• Minimum Power Consumption
• Lowest Processing Time
• Simplified User Interfaces
• Efficient & Great Accurateness

The aforementioned are some of the merits of using MATLAB tools in every technology. As a matter of fact, our articles are being published in the top journals called IEEE and many more. By having the unique ideas we are being acknowledged by the publications. Undoubtedly, you came to the right platform where you can get all the necessary information with visualized demonstrations. Now we can jump into the article’s technical facts.

We are especially good at using the MATLAB in fake currency detections and yielding the best results in it. As this article is concentrated on the MATLAB code for fake currency detection, we have listed MATLAB simulation steps for your better understanding. Shall we get into that phase? Come let us we try to understand them with the brief explanations.

Steps to implement Matlab Code for Fake Currency Detection Research Projects

• Step 1- Image Acquisition
  • Image acquisition can be done with various scanners & cameras
• Step 2- Image Preprocessing
  • Image enriching makes the feature extraction very easy
  • It involves the processes such as color blurring, Grayscale transformation, noise removing & thresholding
  • It ensures the edge detection, color feature evaluation & region of interest in image
• Step 3- Edge Detection & Cropping
  • Binary images having black and white colors involves in the edge detections
  • Segmentation of Region of Interest (ROI), foreground and background determines the cropping
• Step 4- Feature Extraction
  • Extracting necessary features from the ROI takes place here
  • In light conditions aspect ratios and binary image dimensions are found
  • Aspect ratio comparisons with target images in all edges of the currency note
  • Hue Saturation Value (HSV) features are compared with the database values (average)
  • Euclidian distance equation used to find out the mean values of differences among the HSV & target images
• Step 5- Classification & Displaying Results
  • Simplified graphical user interface displays the final results
  • Image (currency) transforms obtained with their currency rates
  • Classification of image is done by methods like SVM, ANN etc.,

The itemized above are the 5 major steps gets involved in the MATLAB simulation for the fake currency detection. As of now, we have seen about the benefits and the simulation steps with bolt and nut points.  In the upcoming areas, we have covered the eminent concepts of the MATLAB code for fake currency detections.  So it is advisable to pay attentions to the each and every section as mentioned in the article.

Generally, these simulation steps are being performed by the application of several methods. Here you may get confusions on the methods followed in the fake currency detections. Our technical team have bulletined you the same for the ease of your understanding. Shall we get into that section guys? Come let us we try to understand them.

Methods for Fake Currency Detection

• Image Preprocessing
  • Image Enrichments
  • Image Cropping
  • Median Filtering
  • Noise Removals
  • Color Space Conversion
  • Histogram Matching
• Image Segmentation
  • Region Methods
  • Model Segmentation
  • Compression Methods
  • Edge Detections
  • OTSU Thresholding
  • K-means Clustering
• Feature Extraction
  • Shape Features
  • Texture Features
  • Color Features
  • HOG Features
  • SHIFT Features
  • SURF Features
• Image Classifiers
  • RBF- Radial Basis Function Classifiers
  • PNN- Probabilistic Neural Network Classifiers
  • BPNN- Back-Propagation Neural Network Classifiers
  • KNN- K-nearest Neighbor Classifiers
  • ANN- Artificial Neural Network Classifiers
  • SVM- Support Vector Machine Classifiers

The aforementioned are some of the methods followed in matlab code for fake currency detection. In fact, we practice these methods in various techniques according to their nature.  As the methods for fake currency detection vast in numbers, we are going to highlight 2 of the above listed methods’ techniques for the ease of your understanding.  In the immediate passage, we are going to demonstrate you the how the shape feature extraction being done.

Shape feature extraction is based on the 2 major elements called regions and contours. They are also been classified under spatial and transform (conversion) domains and involves with the linear, decompositions and parameters. Are you interested to learn? Come let’s we have the further hints in the upcoming section.

Shape Feature Extraction Methods for Fake Currency Detection

• Region- Transform Domain
  • Linear Regions
  • Wavelets, Fourier & Gabor
  • Non-Linear Regions
  • Time Frequency
  • Morphology & Hough
• Region- Spatial Domain
  • Internal Features
  • Shape Matrix
  • Skeletons & Run-length
  • Distance Conversion
  • Decomposition
  • Dendrograms & Polygons
  • Quadtrees & Voronoi
  • Bounding Regions
  • Minimum Bounding Box
  • Convex Hulls
• Contour- Transfer Domain
  • Linear Regions
  • Short Time Fourier
  • Non-Linear Regions
  • Hough
• Contour- Spatial Domain
  • Counter Point Approximations
  • Multi-scale Primitives
  • Active Contours
  • Elliptical & Circle Arcs
  • Regressive Models
  • Parameters
  • Chain Codes
  • Vectors (X,Y)

The itemized above are the shape feature extraction contours and regions. We hope that you would have understood the concepts as of now stated. In the matter of fact, our experts are very familiar with the above mentioned and other techniques and types involved in the fake currency detection. The reason behind having enough knowledge resulted by our continuous experiments and researches in the technical updates.

Deep learning and machine learning concepts are highly utilized in the object detection in the given images. According to our application configurations, we can also make use of other techniques to detect the objects. For instance, analysis of blob and image segmentation retrieves the very simple image properties like image size, resolution, shapes and color. Feature extraction uses the RANSAC to find the image locations. Concurrently, we wanted to demonstrate how the feature extraction is done with several techniques for the ease of you understanding.

Feature Extraction Methods for Fake Currency Detection

• Selection of Features
  • RS- Rank Search
  • CSE- Consistency Subset Evaluator
  • BFS- Best First Search
  • SFS- Sequential Forward Selection
  • PCA- Principle Component Analysis
• Filter Selection of Features
  • BPR- Borda Preference Rule
  • FSE- Filtered Subset Evaluator
  • CAE- Correlation Attribute Evaluator
  • KFDA- Kernel Fisher Discriminant Analysis
  • GR & IG- Gain Ratio & Information Gain
• Data Mining Techniques for Feature Extraction
  • Reinforcement Learning- Learning Automata
  • Regression- Regression Trees
  • Classification- K star, Bayesian Networks & Decision Tree
  • Optimization- Linear Programming
  • Ensemble- AdaBoost
  • Rule System- Fuzzy, Conjuctive & Ripper Rules
  • Clustering-K-means & Hierarchical Clustering

As of now, we have discussed and get brainstormed on the methods followed and 2 of the examples with great exemplifiers. We hope that, you guys were getting the points up to now state. If you still need any clarifications in any of the section mentioned in the article then you can undoubtedly approach our experts without any hesitations. In fact, we are delighted to serve in the fields of technology.

In the following passage, we have also provided the toolboxes that are used in the process of fake currency detections. There are 5 important toolboxes being applied in it. You can also make use of these toolboxes while conducting any research or projects in fake currency detection. Come let’s we have the further explanations in the subsequent passage.

Supported Matlab Toolboxes for Fake Currency Detection

• Deep Learning based Toolbox
• Image Acquisition based Toolbox
• Machine Learning based Toolbox
• Image Processing based Toolbox
• Computer Vision based Toolbox

The aforementioned are some of the key toolboxes used in Matlab code for fake currency detection processes. As a matter of fact, our technical crew is very much aware of every technology edges and tools used in every approach. We thought that, mentioning the MATLAB functions for the fake currency detections here will benefit you hence we covered the final closure as how the MATLAB functions performs according to the features. Are you ready to know about that? Come let’s we have the quick insight!!

Matlab Functions for Fake Currency Detection

Acquisition based Features

• Imaqfind ()- Image acquisition finding “Objects”
• Imaqhwinfo ()- Existing information acquirement “Hardware”
• Imaqreset () – Resetting acquired objects “Discard/Disconnect”
• Imaqregister () – 3^rd party adaptor “Register”

Image Matching based Features

• Matchfeatures ()- Feature findings “Match”
• Matchfeaturesinradius ()- Matching feature finding in given “Radius”

Image Retrieval based Features

• Estimategeometrictransform2D () – 2D geometric conversion estimation in “Pair Points”
• Estimategeometrictransform3D () – 3D geometric conversion estimation in “Pair Points”
• alphablender () – Vision combinations in “Pixels/Images”
• blockmatcher () – Motion estimation in “Images”
• localmaximafinder () – Maxima finding in “Matrices”
• templatematcher () – Template locating in “Images”

Visualization and Display based Features

• Insertmarker () – Marker inserting in “Videos/Images”
• Insertshape () – Shape inserting in “Videos/Images”
• Showmatchedfeatures ()- Displays matched features of “Videos/Images”
• Showshape () – Showcases shapes on “Videos/Images”
• Insertobjectannotation () – Image annotations in “Grayscale/True Color”
• Inserttext ()- Text inserting in “Videos/Images”
• gammacorrector ()- Gamma correction removals in “Videos/Images”
• chromaresampler ()- Chrominance elements in “Up samples/ Down samples”

Image Storing based Features

• Cornerpoints ()- Points storing “Corner”
• ORBpoints () – Key points storing “ORB”
• MSERregions () – Regions storing “MSER”
• SURFpoints () – Interest points storing “SURF”
• KAZEpoints () – Interest points storing “KAZE”
• BRISKpoints () – Interest points storing “BRISK”
• Binaryfeatures () – Feature storing “Binary Vectors”

So far, we have debated on the concepts of MATLAB code for fake currency detection.  We hope that, you would have understood the facts till now stated. In fact, everyone can do projects / researches but injecting innovations and different perceptions need sound knowledge in the concepts. If you are a beginner in these concepts then you can have our technical teams’ suggestions and explanations in each and every of approach of projects / researches.

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