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Currency
   Currency is the means of purchasing
    through trade. Today, currency generally
    refers to printed or minted money.
    Sometimes only paper bills are thought
    of as currency, while other times coins
    are included. Currency involves the
    exchange of goods or services for cash.
Currency counterfeit
   Counterfeiting of money is one of the
    oldest crimes in history. It was a serious
    problem during the 19th century when
    banks issued their own currency. At the
    time of the Civil War, it was estimated
    that one-third of all currency in
    circulation was counterfeit
Currency counterfeit detectors
Why currency validation
system
Because of the availability of mobile in
every hand
The customer didn’t have to buy any of the
previous devices only he pick his mobile
phone up and take a photo for the
currency and through our application he
can determine whether that currency
real or false.
Currencies
   Our system applied on Egyptian
    currencies
System overview
System overview
Used Technologies:
 Matlab
 Microsoft visual studio
 Windows phone developer tools
Processing steps
   Taking a photo for the suspected currency
    via mobile
   Sending the currency image to the server
    to be checked
   Preprocessing of the input image to
    remove noise and background
   Currency value determinant
   Extracting special features
   Checking the validity of the currency
   Show result and send it back to the mobile
Step1: taking a photo for the
suspected currency via mobile
   Check to see if the camera is available on the
    device
   if not available
   Message ===== > The camera not supported
    on the device.
    if available
   Use standard camera if available.
   Otherwise, use front-facing camera on the
    device.
   Start image capture
   Save picture as JPEG to isolated storage.
Step2: Sending the currency image
to the server to be checked

 In this steps the mobile send the
  captured image to the server using
  network
 And the mobile waiting for response
   send image to server by network
    Connecting to a TCP Socket Server
   create a socket and connect to the server
    by using the System.Net.Sockets APIs.
   Send Request to Server for connect by
    Host name and Port Number
   Server listen to any request and Accept On
    Connection
   Client Convert image to Array of bytes
   Send Array to server
 Waiting for Result from Server
 Server Received Byte Array
 Convert array of bytes to Image
 Run processing Operations on This
  Image
Step3: Preprocessing of the input
image to remove noise and
background
 This step concerns with preparing the
  input image for the processing
 ž First, resizing the input image to a
  predefined general size
 ž Second, removing the image noise if
  exist
 finally, removing the currency
 background
Removing noise
   Using blurring filters to remove noise
    from the image
Removing background
   Blobcounter class
Step4: Currency value determinant

 ž In this step we determine the value of
  the currency
 ž There was 2 methods
  1.MSE
  2.comparison method using Surf
  algorithm
1- MSE
   Create a matlab function that calculate the
    mean square error for the spectrum of the
    image
   Binding that matlab function with c#
   ž Crop a rectangle from the upper left
    corner
   ž Apply matlab function that calculate MSE
    in the cropped part
   ž Test the result and get the range
   ž Determine the currency value
MSE limitations
 ž By examining the MSE method we find
  an error rate between 20% & 40%
 That rate come from inference between
  200L.E & 100L.E rates
 ž So, we apply the second method .
Comparison method
 In this method the system determine the
  value of the currency by comparing the
  input currency with a small database
  that contains a number of different
  currencies
  and get the similarity rate for each
  comparison
 ž We determine the currency value by
  getting the highest similarity rate in
  comparison process
Step5: Extracting security features

 After determining the value of
  currency, we need to extract the security
  features for each currency
 Security marks of currencies are those
  marks with which we can determine
  whether the currency is true or false
Some important security marks in
the
Egyptian currency :

   Shiny intermittent tape



   Magnetic security tape



   Eye of Horus
   A print using an ink which its color is
    changeable when tilting the currency
Step6: Checking the validity of the
currency

 In order to check the validity of the
  currency we need to compare the
  extracted security marks with another
  real currency marks that is saved in the
  system
 This process is done using two different
  techniques:
1. Neural Network
2. Surf algorithm
Neural network
 Simply we use xor neural network but with
  changing the inputs of the network to the
  numbers which obtained by counting ones
  exits in rows and columns of special parts
  in the currency like Shiny intermittent tape
  this is called projection.
 In the training process we use back
  propagation algorithm to learn neural by
  using at least one original currency and
  one that is not original .
Neural network
 The ideal output of the original currency
  is 1 and the ideal output of the not
  original one is 0.
 The network stopped when the number
  of epochs is larger than 5000 and the
  error is less than 0.001.then we test the
  network by a new data.
 We compare the output of the network
  by a threshold to determine which it is
  original or not.
Surf algorithm
 Convert the input image to integral
  image
 Applying fast Hessian filter for
  generating interest point
 For each interest point generate surf
  descriptor
 Comparison between surf descriptors in
  each image
Surf descriptors comparison
Step7: Show result and send it
back to the mobile
   In this step the mobile receive the result
    from the server and show a message to
    the user
Limitations
 Camera with more than 5 MP efficiency
 The photo of currency must be taken
  with black background
 Our system is not applied in old versions
  of currency
Team:
 Ahmad Mohammed Akl
 Abdurrahman Mohammed
 Amira Raft Ahmed Elhmamsy
 ž Ibrahim El-Said Mohammed El-Said
  Harhera
 Mai Magdy Mohammad ElKobrosly
 Nada Said El-Said Essa
 ž Nada Mohammed Mohammed Yousef
Thank you

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Currency validation system using mobile

  • 1.
  • 2. Currency  Currency is the means of purchasing through trade. Today, currency generally refers to printed or minted money. Sometimes only paper bills are thought of as currency, while other times coins are included. Currency involves the exchange of goods or services for cash.
  • 3. Currency counterfeit  Counterfeiting of money is one of the oldest crimes in history. It was a serious problem during the 19th century when banks issued their own currency. At the time of the Civil War, it was estimated that one-third of all currency in circulation was counterfeit
  • 5. Why currency validation system Because of the availability of mobile in every hand The customer didn’t have to buy any of the previous devices only he pick his mobile phone up and take a photo for the currency and through our application he can determine whether that currency real or false.
  • 6. Currencies  Our system applied on Egyptian currencies
  • 9. Used Technologies:  Matlab  Microsoft visual studio  Windows phone developer tools
  • 10. Processing steps  Taking a photo for the suspected currency via mobile  Sending the currency image to the server to be checked  Preprocessing of the input image to remove noise and background  Currency value determinant  Extracting special features  Checking the validity of the currency  Show result and send it back to the mobile
  • 11. Step1: taking a photo for the suspected currency via mobile
  • 12. Check to see if the camera is available on the device  if not available  Message ===== > The camera not supported on the device.  if available  Use standard camera if available.  Otherwise, use front-facing camera on the device.  Start image capture  Save picture as JPEG to isolated storage.
  • 13. Step2: Sending the currency image to the server to be checked  In this steps the mobile send the captured image to the server using network  And the mobile waiting for response
  • 14. send image to server by network Connecting to a TCP Socket Server  create a socket and connect to the server by using the System.Net.Sockets APIs.  Send Request to Server for connect by Host name and Port Number  Server listen to any request and Accept On Connection  Client Convert image to Array of bytes  Send Array to server
  • 15.  Waiting for Result from Server  Server Received Byte Array  Convert array of bytes to Image  Run processing Operations on This Image
  • 16. Step3: Preprocessing of the input image to remove noise and background  This step concerns with preparing the input image for the processing  ž First, resizing the input image to a predefined general size  ž Second, removing the image noise if exist  finally, removing the currency  background
  • 17. Removing noise  Using blurring filters to remove noise from the image
  • 18. Removing background  Blobcounter class
  • 19. Step4: Currency value determinant  ž In this step we determine the value of the currency  ž There was 2 methods 1.MSE 2.comparison method using Surf algorithm
  • 20. 1- MSE  Create a matlab function that calculate the mean square error for the spectrum of the image  Binding that matlab function with c#  ž Crop a rectangle from the upper left corner  ž Apply matlab function that calculate MSE in the cropped part  ž Test the result and get the range  ž Determine the currency value
  • 21. MSE limitations  ž By examining the MSE method we find an error rate between 20% & 40%  That rate come from inference between 200L.E & 100L.E rates  ž So, we apply the second method .
  • 22. Comparison method  In this method the system determine the value of the currency by comparing the input currency with a small database that contains a number of different currencies and get the similarity rate for each comparison  ž We determine the currency value by getting the highest similarity rate in comparison process
  • 23. Step5: Extracting security features  After determining the value of currency, we need to extract the security features for each currency  Security marks of currencies are those marks with which we can determine whether the currency is true or false
  • 24. Some important security marks in the Egyptian currency :  Shiny intermittent tape  Magnetic security tape  Eye of Horus
  • 25. A print using an ink which its color is changeable when tilting the currency
  • 26. Step6: Checking the validity of the currency  In order to check the validity of the currency we need to compare the extracted security marks with another real currency marks that is saved in the system  This process is done using two different techniques: 1. Neural Network 2. Surf algorithm
  • 27. Neural network  Simply we use xor neural network but with changing the inputs of the network to the numbers which obtained by counting ones exits in rows and columns of special parts in the currency like Shiny intermittent tape this is called projection.  In the training process we use back propagation algorithm to learn neural by using at least one original currency and one that is not original .
  • 28. Neural network  The ideal output of the original currency is 1 and the ideal output of the not original one is 0.  The network stopped when the number of epochs is larger than 5000 and the error is less than 0.001.then we test the network by a new data.  We compare the output of the network by a threshold to determine which it is original or not.
  • 29. Surf algorithm  Convert the input image to integral image  Applying fast Hessian filter for generating interest point  For each interest point generate surf descriptor  Comparison between surf descriptors in each image
  • 31. Step7: Show result and send it back to the mobile  In this step the mobile receive the result from the server and show a message to the user
  • 32. Limitations  Camera with more than 5 MP efficiency  The photo of currency must be taken with black background  Our system is not applied in old versions of currency
  • 33. Team:  Ahmad Mohammed Akl  Abdurrahman Mohammed  Amira Raft Ahmed Elhmamsy  ž Ibrahim El-Said Mohammed El-Said Harhera  Mai Magdy Mohammad ElKobrosly  Nada Said El-Said Essa  ž Nada Mohammed Mohammed Yousef