Fake Currency Detection Using Matlab

In general, fake currencies are detected by comparing the features of a given physical currency note with the original banknote which is known as fake currency detection. In the world of the digital era, technologies are vastly developing along with fraudulent activities, particularly in finance. Currently, fake currencies are growing on a large scale due to advanced technologies. So, the need for efficient fake currency detection applications is highly increasing in the banking sector. When the banknote is passed over UV light at 365 nanometers wavelength, it will be visible clearly. 

If you are searching for the best project in fake currency detection using Matlab, then you can find this article is more useful for you!!!

MATLAB is a software tool that provides all necessary libraries and functions to develop fake currency detection applications. These applications involve two main processes as recognition and classification. As a result, it becomes a favorable process for visually impaired people. Also, it is useful in day-to-day money transactions in hospitals, banks, traveling, government offices, etc. 

Now, we can see in what way the fake currency note is detected. To detect the fake currency note, focus on several interruptions over the thread line. Based on the interruption count, the currency note is classified as fake or real. When the number of interruptions is “zero”, the currency note is identified as “real”. Further, the authentication of the currency note is verified by the following elements.

How fake currency is detected?

• Watermark
• Security Thread
• Identification Mark
• Latent image
• Serial Number 

Research Gaps in Fake Currency Detection Using Matlab 

Although identification of fake currencies is a challenging task, it is getting improvising automated currency note recognition system is getting improvised more in recent years. As a result, it gains the attention of current research scholars in large volume. Since fake currency is becoming a challenging issue in many countries. In the past, only limited print houses have existed but now anyone can easily print fake currencies using a laser printer. So, many pieces of research successfully done to accurately detect the fake currencies and also currently using detectors in the bank, shopping mall, colleges, etc.

Although these detectors are useful it’s not applicable for common people due to expensiveness. In order to overcome this issue, the current researches are focusing on fake currency detection using the Matlab tool. Since Matlab tool enables to development of any kind of image processing techniques. 

On knowing the importance of this field, several currency detection approaches are widely introduced and more are in the developing stage. So, the image processing and machine learning fields are more benefitted. Here, we have given you a few important learning algorithms that highly used these fields, especially in currency detection.

Best Techniques for Fake Currency Detection

• Image Processing
  • Image De-noising / Preprocessing
    • Wiener Filtering
    • Median Filtering
    • Wavelet Denoising
    • Weighted Median Filtering
    • Morphological Techniques
  • Feature Extraction
    • Global Transformation
    • Series Expansion
    • Geometrical-based
    • Statistical-based
    • Topological-based
  • Image Improvement
    • Contrast Enhancement
    • Normalization
    • Histogram Equalization
  • Image Segmentation
    • Region / Graph-based
    • Atlas / Library-based
    • Deformable Model-based
    • Statistical Model-based
    • Histogram-based
• Machine Learning
  • Unsupervised ML Techniques
    • Anomaly Detection
    • Apriori Algorithm
    • Auto-Encoders (AE)
    • Hybrid Techniques
    • Hebbian Learning Rule
    • Expectation Maximization (EM)
    • Deep Belief Networks (DBN)
    • Principal Component Analysis (PCA)
    • Generative Adversarial Networks (GAN)
    • Singular Value Composition (SVC)
    • Non-Negative Matrix Factorization (NNMF)
    • Independent Component Analysis (ICA)
    • K-Means and Hierarchical Clustering
  • Supervised ML Techniques
    • Naïve Bayes (NB)
    • Decision Tree (DT)
    • Support Vector Machine (SVM)
    • K-Nearest Neighbor (KNN)
    • Multi-Regression (MR)
    • Convolutional Neural Network (CNN)
    • Linear / Logistic Regression (LR)
    • Linear Discriminant Analysis (LDA)

What are the best datasets for fake currency detection?

To begin the development of your proposed work, initially create a dataset (old and new) for performing training and testing processes over currency. Make sure that have collected top-quality images in your dataset. For illustration, here we have given you two important datasets that are principally used for fake currency detection using Matlab tool. Moreover, we also support you in other kinds of datasets.

Indian Currency Dataset

• Dataset – 4650+ images
• Source – Moto X-Play mobile with 21MP camera
• Image Size – 3006×5344 (portrait) and 5344×3006 (landscape)
• Category – 10 Classes of Indian Banknotes
  • 10 Classes – 10 2000 New, 500 New, 200 New, 100 Old, 100 New, 50 Old, 50 New, 20, 10 Old, 10 New
• Specification – Enhance the data size by data augmentation (Distortion, Rotate90, Zoom, Flip, Rotate270, Zoom, Tilt). As a result, you can get 11650+ images
• Purpose – Real / Fake currency detection

Indian and Thai Banknotes Dataset

• Dataset – 2900+ images (1900+ Indian banknotes and 1000+ Thaai banknotes)
• Source – Smartphone rear camera
• Category – 10 Classes for Indian banknotes and 5 Classes for Thai banknotes
  • 10 classes – 2000 New, 500 New, 200 New, 100 Old, 100 New, 50 Old, 50 New, 20, 10 Old, 10 New
  • 5 classes – 2000, 500, 100, 50, 20
• Specification – These images has various chaotic background and lightings
• Purpose – Real / Fake currency detection

In addition, we have also given you the significant functions that are widely used in fake currency detection using Matlab. Our developers have well-equipped knowledge on handling Matlab tools in every module and toolboxes. So, we are skillful to work with all necessary functions by default. By the by, each is assigned with a unique task to perform over image processing. We are adept to simplify code development by choosing appropriate functions. Let’s have look over on primary functions required for fake currency

Matlab Functions for Fake Currency Detection

• Dependent Data
  • dither – To represent binary based grayscale image
• Independent Data
  • Imlincomb – To represent image in linear combo
  • Intlut – To transform integer values based on lookup table
  • Imadjust – To modify the values of intensity
• Data Distribution
  • edge – To identify edge of the grayscale image
  • conv2 – To represent 2D image convolution
  • imregionalmax – To represent regional maximization of image
  • ordfilt2 – To represent 2D order-statistic extraction / filter
  • imerode / imdilate – To represent erosion or dilation of grayscale image
  • mean2 – To represent matrix elements average
• Dependent Algorithm
  • radon – To represent radon conversion
  • bwdist – To represent binary image’s Euclidean distance conversion

Furthermore, we have also given you the basic toolboxes that are highly imported for fake currency detection using Matlab Projects. Here, each toolbox has a specific set of libraries and functions to support certain processes. For instance: Through the machine learning toolbox, we can perform all supervised, semi-supervised and unsupervised learning techniques. Our developers are smart to identify the best-fitting toolboxes for your project based on project intentions. So, connect with us to know the appropriate dataset, tool, functions, and toolboxes for your selected project.

Matlab Toolboxes for Fake Currency Detection

• Image acquisition toolbox
• Deep learning toolbox
• Machine learning toolbox
• Image processing toolbox

For illustration purposes, here we have given you the sample development of the fake currency detection project. In this, we have mentioned to you the essential software requirements for the proposed fake currency detection project. Further, it also included the primary tasks of the project starting from image collection to performance evaluation. Similarly, we also provide you with the development plan before start implementing your project. This plan includes a step-by-step project workflow, basic software requirements, hardware requirements, and performance metrics that are used for evaluating the developed system at the end of project execution. Firstly, let’s have a fast glance at fundamental software requirements. 

Fake Currency Detection Using MATLAB

Software Requirements

• Operating System
  • Linux and Windows
• Programming Language
  • Matlab
• Database
  • Keep the record of important features of currency like serial number, intensity, color, etc.
• Image Processing Toolbox
  • Enables you to perform all basic and advanced image processing operations
  • Designed to implement functions for display, fundamental import and export
  • For instance: mathematical modification, noise reduction, image deblurring, image registration, image segmentation, characteristics identification, etc.

Secondly, let’s have a quick look over the primary tasks of the general fake currency detection project. Further, it comprises other operations based on your proposed project objectives.

Fake Currency Detection Projects

• Image Collection
  • Pass over the different color lights over collected image for hyperspectral imaging at different wavelengths
  • For instance: Red LED light, Green LED light, Normal LED Bulb, Blue LED light, Ultraviolet (UV) light
  • Wavelength – between 360 nm and 800 nm
• Image Preprocessing
  • Apply techniques to remove unwanted background data for better image transformation
• Feature Extraction
  • Identify various key features of input currency note and compare with real not for classifying notes (real or fake)
• Feature Selection
  • Compute the entropy of selected features and aspect ratio for input note for categorizing currencies
• Fake Currency Recognition
  • Implement fake currency detection model using image processing techniques
  • Use deep neural network algorithm to train the dataset
  • Extract the front and back currency features using convolutional neural network
  • In overall, deep learning with CNN enhances the accuracy of currencies recognition model
• Performance Evaluation
  • Although textural background looks complex and has same intensity-levels, it acquires best and accurate results

Last but not least, we are here to provide you innovative project topics for fake currency detection using Matlab with Last but not least, we are here to provide you with innovative project topics for fake currency detection using Matlab with code development support. We ensure that we develop your project with the best dataset, functions, techniques, and performance metrics. Moreover, we also provide you with well-organized project dissertation writing for our handhold final year students. As well as, we also offer proposal writing, literature review writing, paper writing, paper publication, and thesis writing for our handhold scholars. To know more about our service, communicate with our team.