Fingerprint Analysis (part 1) Pavel Mrázek. What is fingerprint Ridges, valleys Singular points...

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Fingerprint Analysis (part 1) Pavel Mrázek

Transcript of Fingerprint Analysis (part 1) Pavel Mrázek. What is fingerprint Ridges, valleys Singular points...

Fingerprint Analysis(part 1)

Pavel Mrázek

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

Minutia examples

SensingTraditional (off line): rolled ink impression

+ paper scan

• Plus: big area

• Minuses: – Inconvenient– Distortion– Too much/little ink

SensingOptical sensors

SensingOptical sensors

• Good: large area possible, good image quality, contactless scanning available

• Bad: size

SensingSilicon sensors

• Capacitive

• Electric field

• Thermal

SensingSilicon sensors

• Good image quality, small form factor

• Price proportional to size

SensingSilicon sensors

• Area

• Swipe

Fingerprint types

Minutia detection overview

Orientation fieldOrientation field (or ridge flow)

estimation:

• Crucial step before

image enhancement

• Various methods:– Gradient-based– Gabor filters– FFT

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

Orientation estimationStructure 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)

Orientation estimationStructure tensor

• system of 2 orthogonal eigenvectors

• shows dominant direction

Orientation estimation

Orientation estimation

Orientation estimation• Problematic images

• Solution– Enforce smoothness– Use prior knowledge

Orientation model

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