Currency   Currency is the means of purchasing    through trade. Today, currency generally    refers to printed or minted...
Currency counterfeit   Counterfeiting of money is one of the    oldest crimes in history. It was a serious    problem dur...
Currency counterfeit detectors
Why currency validationsystemBecause of the availability of mobile inevery handThe customer didn’t have to buy any of thep...
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   ...
Step1: taking a photo for thesuspected currency via mobile
   Check to see if the camera is available on the    device   if not available   Message ===== > The camera not support...
Step2: Sending the currency imageto the server to be checked In this steps the mobile send the  captured image to the ser...
   send image to server by network    Connecting to a TCP Socket Server   create a socket and connect to the server    b...
 Waiting for Result from Server Server Received Byte Array Convert array of bytes to Image Run processing Operations o...
Step3: Preprocessing of the inputimage to remove noise andbackground This step concerns with preparing the  input image f...
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...
1- MSE   Create a matlab function that calculate the    mean square error for the spectrum of the    image   Binding tha...
MSE limitations ž By examining the MSE method we find  an error rate between 20% & 40% That rate come from inference bet...
Comparison method In this method the system determine the  value of the currency by comparing the  input currency with a ...
Step5: Extracting security features After determining the value of  currency, we need to extract the security  features f...
Some important security marks intheEgyptian 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 thecurrency In order to check the validity of the  currency we need to compare the  extra...
Neural network Simply we use xor neural network but with  changing the inputs of the network to the  numbers which obtain...
Neural network The ideal output of the original currency  is 1 and the ideal output of the not  original one is 0. The n...
Surf algorithm Convert the input image to integral  image Applying fast Hessian filter for  generating interest point F...
Surf descriptors comparison
Step7: Show result and send itback to the mobile   In this step the mobile receive the result    from the server and show...
Limitations Camera with more than 5 MP efficiency The photo of currency must be taken  with black background Our system...
Team: Ahmad Mohammed Akl Abdurrahman Mohammed Amira Raft Ahmed Elhmamsy ž Ibrahim El-Said Mohammed El-Said  Harhera M...
Thank you
Currency validation system using mobile
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Currency validation system using mobile

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اختبار العمله المصريه من خلال الموبيل

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Currency validation system using mobile

  1. 1. 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.
  2. 2. 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
  3. 3. Currency counterfeit detectors
  4. 4. Why currency validationsystemBecause of the availability of mobile inevery handThe customer didn’t have to buy any of theprevious devices only he pick his mobilephone up and take a photo for thecurrency and through our application hecan determine whether that currencyreal or false.
  5. 5. Currencies Our system applied on Egyptian currencies
  6. 6. System overview
  7. 7. System overview
  8. 8. Used Technologies: Matlab Microsoft visual studio Windows phone developer tools
  9. 9. 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
  10. 10. Step1: taking a photo for thesuspected currency via mobile
  11. 11.  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.
  12. 12. Step2: Sending the currency imageto 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
  13. 13.  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
  14. 14.  Waiting for Result from Server Server Received Byte Array Convert array of bytes to Image Run processing Operations on This Image
  15. 15. Step3: Preprocessing of the inputimage to remove noise andbackground 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
  16. 16. Removing noise Using blurring filters to remove noise from the image
  17. 17. Removing background Blobcounter class
  18. 18. 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
  19. 19. 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
  20. 20. 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 .
  21. 21. 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
  22. 22. 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
  23. 23. Some important security marks intheEgyptian currency : Shiny intermittent tape Magnetic security tape Eye of Horus
  24. 24.  A print using an ink which its color is changeable when tilting the currency
  25. 25. Step6: Checking the validity of thecurrency 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 Network2. Surf algorithm
  26. 26. 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 .
  27. 27. 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.
  28. 28. 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
  29. 29. Surf descriptors comparison
  30. 30. Step7: Show result and send itback to the mobile In this step the mobile receive the result from the server and show a message to the user
  31. 31. 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
  32. 32. 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
  33. 33. Thank you

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