50409621003 fingerprint recognition system-ppt


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50409621003 fingerprint recognition system-ppt

  2. 2. AbstrAct  The minutiae are ridge endings or bifurcations on the fingerprints. They, including their coordinates and direction, are most distinctive features to represent the fingerprint.  Most fingerprint recognition systems store only the minutiae template in the database for further usage.  The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets.
  3. 3.  This kind of minutiae-based fingerprint recognition systems consists of two steps, i.e., minutiae extraction and minutiae matching.  In the minutiae matching process, the minutiae feature of a given fingerprint is compared with the minutiae template, and the matched minutiae will be found out.  The template used for fingerprint recognition is further utilized in the matching stage to enhance the system’s performance.
  4. 4.  These templates are been stored in the database for further processing.  Specific unique id is generated for each template stored.  The id is stored in the database along with the template locations.  Next is the verification process where you need to provide the id for comparison.  The given id is being compared with the id’s in the database and matched with the corresponding template of that id.
  5. 5.  A message is being displayed on successful matching along with the score.  Otherwise an failure message is displayed indicating match not found.  In the identification stage we need not specify an id. Click on the Identify button which searches for any match and provides the corresponding result.
  6. 6.  The database records can also be deleted through the application.  Auto extract and Auto identify check boxes can be selected if need which automatically extracts and identifies the matching image  It is also possible to load an image and compare it with the database images.  The colors of the template extraction can also be changed.
  7. 7. OrGANIZAtIONAL PrOFILE OrAtOr sOLUtIONs  The name “Orator”– A Good Speaker, One who speaks well in public. A good speaker has lot of followers. Like that, We people are good speaker by our work. We satisfy our clients by our services. And we are here to provide Intelligent SKILLS  Banking & Real Estate  Finance & Insurance
  8. 8. o Hospital and health care industry o Web Applications o Office Automation o Manufacturing Retailing
  9. 9. HArDWArE rEQUIrEMENts  Processor PENTIUM IV  RAM 128 MB  Hard Disk 40 GB
  10. 10. sOFtWArE rEQUIrEMENts  Browser Internet Explorer  Server side scripting Java  Database Ms-Access  Client side scripting HTML
  11. 11. ExIstING systEM  The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets.  In existing system, the Sparse areas are not considered. So the result may not obtain correctly. And the matching will be difficult to get an absolute result.
  12. 12. FingerPrint View Red Spots Minutiae
  13. 13. PrOPOsED systEM  In Proposed system, we considered the sparse area and the fingerprint’s orientation field is reconstructed from minutiae and further utilized in the matching stage to enhance the system’s performance.
  15. 15. DaTa FlOw DIaGRam Show the matching Score Thump Impression Extract Particular Identify Entire Verification Database If Matches Show the Particular ID No Does Not Exist False True If Matches Does not match False True Enroll
  16. 16. mODulE lIST  Minutiae Template  Minutiae Matching  Effective Region Estimation  Orientation Field Matching  Fusing Matching
  17. 17. mINuTIaE TEmPlaTE  Minutiae templates are a fraction of the size of fingerprint images, require less storage memory and can be transmitted electronically faster than images.
  18. 18. mINuTIaE maTCHING  In this module we matches the fingerprint minutiae by using both the local and global structures of minutiae.  The local structure of a minutia describes a rotation and translation invariant feature of the minutia in its neighborhood.  It is used to find the correspondence of two minutiae sets and increase the reliability of the global matching.  The global structure of minutiae reliably determines the uniqueness of fingerprint. Therefore, the local and global structures of minutiae together provide a solid basis for reliable and robust minutiae matching.
  19. 19. (a) X Person’s Fingerprint (b) X Person’s Fingerprint for verification Matching Stage
  20. 20. EFFECTIvE REGION ESTImaTION  we can extract the effective region by finding the smallest envelope that contains all the minutiae points. For an When only having minutiae feature, we can extract the effective region by only using minutiae illustration. Here, we put the original image together for the convenience to give a visual sense.
  21. 21. Estimation of Effective Region
  22. 22. ORIENTaTION FIElD maTCHING  To compare two fingerprints’ orientation field, the first step is alignment of these two fingerprints. The alignment is mainly based on minutiae information . Here we choose the Hough transform based approach to finish the alignment due to its simplicity.  Hough Transform (HT) is one of the most common methods for detecting shapes (lines, circles, etc.) in binary or edge images. Its advantage is its ability to detect discontinuous patterns in noisy images, but it requires a large amount of computing power.
  23. 23. FuSING maTCHING  A variety of combination rules have been proposed. It has shown that matching accuracy can be improved by combining independent matchers using Neyman– Pearson rule. Here, we will also use Neyman–Pearson rule for the task.  Matching of test fingerprint with template is done in Neyman-Pearson rule. Two sets of minutiae are compared. If matching score is found, then the fingerprint is matched with template. Otherwise does not matched with the template.
  24. 24. INPuT aND OuTPuT SCREENS
  25. 25. Main Page
  26. 26. Authentication
  27. 27. Load BMP Image
  28. 28. Extract Template
  29. 29. Enroll
  30. 30. Reports
  31. 31. Verify
  32. 32. Verify
  33. 33. Identify
  34. 34. ClearDatabase
  35. 35. ClearLog
  36. 36. Benefits of the project  The project can be used for security purposes.  E.g. : Attendance , voter registration, Crime Investigation, forensic fingerprint searching.
  37. 37. CONCLUSION Orientation field is important for fingerprint representation. In order to utilize the orientation information in automatic fingerprint recognition systems which only stores minutiae feature. So we can reduce the usage of memory and enhancing the performance of system. We also utilize the reconstructed orientation field information into the matching stage. We can reduce the effect of wrongly detected minutiae. A fingerprint matching based on orientation field is used to combine with conventional minutiae matching for real applications.
  38. 38. THANK YOU