Fingerprints images

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Fingerprints images

  1. 1. Fingerprints
  2. 2. Fingerprints are the most popular and studied biometrics features. Their stability and uniqueness make the fingerprint identification system extremely reliable and useful for security applications.
  3. 3. Fingerprint details :
  4. 4. There are more than 120 fingerprint classes(patterns) The five most common classes are :
  5. 5. • Arch: ridges enter from one side, rise to form a small bump, then go down and to the opposite side. No loops or delta points are present.
  6. 6. •TentedArch: similar to the arch except that at least one ridge has high curvature, thus one core and one delta points.
  7. 7. • Left loop: one or more ridges enter from one side, curve back, and go out the same side they entered. Core and delta are present.
  8. 8. • Right loop: same as the left loop, but different direction.
  9. 9. • Whorl: contains at least one ridge that makes a complete 360 degree path around the center of the fingerprint. Two loops (same as one whole) and two deltas can be found.
  10. 10. There are some unclassified fingerprints
  11. 11. FINGERPRINT RECOGNITION The fingerprint recognition problem can be grouped into three sub-domains: 1.fingerprint enrollment 2.verification 3.fingerprint identification The following are Fingerprint Recognition Techniques:
  12. 12. A. Minutiae Extraction Technique: Most of the finger-scan technologies are based on Minutiae. Minutia-based techniques represent the fingerprint by its local features, like terminations and bifurcations. Minutiae are extracted from the two fingerprints and stored as sets of points in the 2-D plane, then the number of points and points coordinates are compared to each other.
  13. 13. B. Pattern Matching or Ridge Feature Based Techniques: Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images be aligned in the same orientation. To do this, the algorithm finds a central point in the fingerprint image and centers on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned fingerprint image.
  14. 14. And There are many other complicated techniques to recognize fingerprints.
  15. 15. Minutiae Extraction Technique:
  16. 16. Processing • the basic problem when finding consist of deciding if the pixel evaluated belong to the ridge or not • Next ,we will present the processing steps in order to adapt the input fingerprint image to the next block requirement and to convert it in a set of these interest lines ,named Ridge Map.
  17. 17. 1- Image Enhancement and noise reduction applying direction filters. 2 – Binarized the image using the Otsu method to obtain the best performance threshold 3- Thinning algorithm by means of mathematical morphology for extracting a set of interset line, obtaining the Thinned Ridge Map .
  18. 18. 4 -Deputation of the ridge map: involves the removal of the spurious elements and join the broken line using a smoothing procedure. This depuration process is carried out by simple rules like : - to remove small isolated line - to merge all line who have end points with similar direction and the distance between them is small
  19. 19. The minutiae points are determined by scanning the local neighbourhood of each pixel in the ridge thinned image, using a 3×3 window a)
  20. 20. The CN value is then computed, which is defined as half the sum of the differences between pairs of neighboring pixels pi and pi+1
  21. 21. minutiae are major features of a fingerprint , using which comparisons of one print with another can be made. Minutiae include: * Ridge ending – the abrupt end of a ridge * Ridge bifurcation (Fork)– a single ridge that divides into two ridges * Island – a single small ridge inside a short ridge or ridge ending that is not connected to all other ridges * Crossover or bridge – a short ridge that runs between two parallel ridges * Delta – a Y-shaped ridge meeting * Core – a U-turn in the ridge pattern
  22. 22. Ridge ending Fork Scar (Dot)Island Delta Crossover
  23. 23. Riyad Bustami Mohammed Dwekat

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