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    Fingerprints images Fingerprints images Presentation Transcript

    • Fingerprints
    • 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.
    • Fingerprint details :
    • There are more than 120 fingerprint classes(patterns) The five most common classes are :
    • • 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.
    • •TentedArch: similar to the arch except that at least one ridge has high curvature, thus one core and one delta points.
    • • 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.
    • • Right loop: same as the left loop, but different direction.
    • • 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.
    • There are some unclassified fingerprints
    • 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:
    • 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.
    • 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.
    • And There are many other complicated techniques to recognize fingerprints.
    • Minutiae Extraction Technique:
    • 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.
    • 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 .
    • 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
    • The minutiae points are determined by scanning the local neighbourhood of each pixel in the ridge thinned image, using a 3×3 window a)
    • 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
    • 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
    • Ridge ending Fork Scar (Dot)Island Delta Crossover
    • Riyad Bustami Mohammed Dwekat