Presentation is loading. Please wait.

Presentation is loading. Please wait.

Fingerprint Analysis (part 1) Pavel Mrázek. What is fingerprint Ridges, valleys Singular points –Core –Delta Orientation field Ridge frequency.

Similar presentations


Presentation on theme: "Fingerprint Analysis (part 1) Pavel Mrázek. What is fingerprint Ridges, valleys Singular points –Core –Delta Orientation field Ridge frequency."— Presentation transcript:

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

20 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


Download ppt "Fingerprint Analysis (part 1) Pavel Mrázek. What is fingerprint Ridges, valleys Singular points –Core –Delta Orientation field Ridge frequency."

Similar presentations


Ads by Google