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An Identity-authentication system using fingerprints By Jain, Hong, Pankanti and Bolle.

An Identity-authentication system using fingerprints By Jain, Hong, Pankanti and Bolle. Jesse Twardus Chris Del Checcolo Dec. 2 nd 2004. Presentation Overview. Overview of fingerprint based biometrics systems Fingerprint Minutia Extraction Algorithm Fingerprint Matching Algorithm

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An Identity-authentication system using fingerprints By Jain, Hong, Pankanti and Bolle.

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  1. An Identity-authentication system using fingerprintsBy Jain, Hong, Pankanti and Bolle. Jesse Twardus Chris Del Checcolo Dec. 2nd 2004

  2. Presentation Overview • Overview of fingerprint based biometrics systems • Fingerprint Minutia Extraction Algorithm • Fingerprint Matching Algorithm • Advantages / Disadvantages • Results

  3. Fingerprint Based Biometric Systems Image Acquisition Feature Extraction Feature Matching Outcome

  4. Biometrics overview • Identifying an individual based on his/her physiological or behavioral characteristics. • Examples: Iris, Gait, hand geometry, face recognition, speech,DNA,handwriting and fingerprint. • Uses: banking, Physical Access, Information systems, Voter registration and immigration.

  5. Qualities for a successful biometric • Universality • Uniqueness • Permanence • Collectability • Performance • Acceptability • Circumvention

  6. Focus on Fingerprints • Oldest biometric • Characteristics • Ridges • Valleys • Minutiae • Core • Delta

  7. Focus on Fingerprints cont. • Advantages • Ridges and Valleys are different for each finger and person • Fingerprints can be easily classified: • Left loop, right loop, whorl, arch, tented arch • Ridges are permanent…and valleys are too.

  8. Steps in the algorithm • Acquire image • Estimate orientation field • Feature extraction • Matching

  9. Acquire image • Problems • Inconsistent contact. • Nonuniform contact • Different finger orientations • Distortion • The algorithm attempts to account for the above problems that can occur during image acquisition

  10. Orientation Field Est. Part 1. • Image is divided into WxW sized blocks • Compute the gradient of each pixel • Change in intensity at each pixel • Estimate the orientation field of each block by using the following functions

  11. Orientation Field Est. Part 2. • Compute the consistency level of the orientation field. • if the consistency level is above a threshold Tc, re-estimate the orientation of this region at a lower resolution level (smaller block) d if(d=(Θ’-Θ+360)mod 360)< 180 d – 180 otherwise

  12. Ridge Detection

  13. Image Thinning • After ridge detection, the resulting image will have holes and “speckles” • These are smoothed in the following manner: • If the angle formed by a ridge branch is between 70 and 110 degrees and the length of the branch is less than 20 pixels, the branch is removed. • If the break in a ridge is shorter than 15 pixels and no other ridges pass though it, the break is connected • Then the image is thinned

  14. Minutiae Detection • Add up all 8 of the pixel’s neighbors • If the sum = 1, pixel is a ridge ending • If the sum > 2, pixel is a ridge bifurcation • For each minutiae, we will have the X and Y coordinates, the orientation and its associated ridge segment.

  15. Matching • The images are aligned using the extracted ridges in the following manner: • Ridges are matched by the formula • If S is larger than 0.8 then the two ridges are declared a match

  16. Matching cont. • After 2 ridges are matched, estimate the transformation via the following formula.Δx = xd-xD • Δy = yd-yD • Δθ=

  17. Matching cont. • Translate and rotate all the input minutiae with respect to the reference minutiae

  18. Template & Input Y Template2 X

  19. Matching cont. • The minutiae points are represented as a string in the polar coordinate system. • The strings are matched using the following dynamic programming algorithm

  20. Matching cont. • An adaptive bounding box is used when an inexact match is found • The bounding box is adjusted in the following manner

  21. Paper Results • Average False Accept rate: 0.026% • Average False Reject rate: 13.34% • Filterbank • FAR = 1.92 • FRR = 10.0

  22. Advantages • Robust, simple and fast verification system • 1.3 seconds for minutiae extraction and matching on a Sun ULTRA 1 • (170 mhz and 256 megs memory) • Filter bank requires approximately 3 seconds on a Sun ULTRA 10 • Small template size: store minutiae points and ridges, not images. Ridges stored as a 1D discrete signal.

  23. Disadvantages • High FRR. Approximately 13 out of 100 people will be rejected. • For a high security location, this rate could be considered ideal.

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