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Fingerprint Analysis (part 1) Pavel Mr ázek

Fingerprint Analysis (part 1) Pavel Mr ázek. What is fingerprint. Ridges, valleys Singular points Core Delta Orientation field Ridge frequency. Fingerprint classes. Small scale: Minutia. 150 types in theory 7 used by human experts 2 types for the machine: Ending Bifurcation.

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Fingerprint Analysis (part 1) Pavel Mr ázek

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  1. Fingerprint Analysis (part 1) Pavel Mrázek

  2. What is fingerprint • Ridges, valleys • Singular points • Core • Delta • Orientation field • Ridge frequency

  3. Fingerprint classes

  4. Small scale: Minutia • 150 types in theory • 7 used by human experts • 2 types for the machine: • Ending • Bifurcation

  5. Minutia examples

  6. Sensing Traditional (off line): rolled ink impression+ paper scan • Plus: big area • Minuses: • Inconvenient • Distortion • Too much/little ink

  7. Sensing Optical sensors

  8. Sensing Optical sensors • Good: large area possible, good image quality, contactless scanning available • Bad: size

  9. Sensing Silicon sensors • Capacitive • Electric field • Thermal

  10. Sensing Silicon sensors • Good image quality, small form factor • Price proportional to size

  11. Sensing Silicon sensors • Area • Swipe

  12. Fingerprint types

  13. Minutia detection overview

  14. Orientation field Orientation field (or ridge flow) estimation: • Crucial step before image enhancement • Various methods: • Gradient-based • Gabor filters • FFT

  15. Orientation estimation • Gradient direction • local characteristics • same ridge orientation, opposite gradients • more global view needed • Classical solution: Structure tensor(second moment matrix, interest operator) • start from a 2x2 matrix(positive semidefinite) • safe to average information

  16. Orientation estimation Structure tensor • Local: • Larger scale: average componentwise(Gaussian window, linear/nonlinear smoothing) • 2 nonnegative eigenvalues • both small: backgroung / low contrast • one big, one small: regular ridge area • both big: multiple orientations (core, delta, scar)

  17. Orientation estimation Structure tensor • system of 2 orthogonal eigenvectors • shows dominant direction

  18. Orientation estimation

  19. Orientation estimation

  20. Orientation estimation • Problematic images • Solution • Enforce smoothness • Use prior knowledge

  21. Orientation model

  22. References • Maltoni et al.: Handbook of Fingerprint Recognition. Springer 2003. • Maltoni. A tutorial on fingerprint recognition. In LNCS 3161, Springer 2005. • Hong, Wan, Jain. Fingerprint image enhancement: algorithm and performance evaluation. IEEE PAMI 1998. • Zhou, Gu. A model-based method for the computation of fingerprints’ orientation field. IEEE TIP 2004. • Weickert. Coherence enhancing shock filters. DAGM 2003. • Contact: mrazekp -at- cmp.felk.cvut.cz

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