An Identity-authentication system using fingerprints

By Jain, Hong, Pankanti and Bolle.

Jesse TwardusChris Del Checcolo

Dec. 2nd 2004

Presentation Overview

I. Overview of fingerprint based biometrics systems

II. Fingerprint Minutia Extraction Algorithm

III. Fingerprint Matching Algorithm

IV. Advantages / Disadvantages

V. Results

Fingerprint Based Biometric Systems

Image

Acquisition

Feature

Extraction

Feature

MatchingOutcome

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.

Qualities for a successful biometric

Universality Uniqueness Permanence Collectability Performance Acceptability Circumvention

Focus on Fingerprints

Oldest biometric Characteristics

Ridges Valleys Minutiae

Core Delta

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.

Steps in the algorithm

Acquire image Estimate orientation field Feature extraction Matching

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

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

2

2

2

2

),(),(2),(

wi

wiu

wj

wjv

vuGyvuGxjiVx

2

2

2

2

22 )),(),((),(

wi

wiu

wj

wjv

vuyGvuxGjiVy

)),(

),((tan

2

1, 1

jiVy

jiVxji

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)

Dji

jijijiC)','(

2|),()','(|),(

|'|d if(d=(Θ’-Θ+360)mod 360)< 180

d – 180 otherwise

Ridge Detection

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

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.

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

L

i

i

l

i

ii

iDd

DdS

0

22

0

Matching cont.

After 2 ridges are matched, estimate the transformation via the following formula.Δx = xd-xD

Δy = yd-yD

Δθ= )(1

0

L

i

iiL

Matching cont.

Translate and rotate all the input minutiae with respect to the reference minutiae

X

Y

Template2 Template & Input

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

Matching cont.

An adaptive bounding box is used when an inexact match is found

The bounding box is adjusted in the following manner

Paper Results

Average False Accept rate: 0.026% Average False Reject rate: 13.34%

Filterbank FAR = 1.92 FRR = 10.0

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.

Disadvantages

High FRR. Approximately 13 out of 100 people will be rejected.

For a high security location, this rate could be considered ideal.