Fingerprint Minutiae Matching Algorithm using Distance Histogram of Neighborhood Presented By: Neeraj Sharma M.S. student, Dongseo University, Pusan South.

Fingerprint Minutiae Matching Algorithm using Distance Histogram of Neighborhood

Presented By: Neeraj Sharma

M.S. student, Dongseo University, Pusan South Korea

CGIV 19SEP07Presented by:[email protected]

Abstract

Paper proposes a novel approach for matching of minutiae point in fingerprint images.

The key concept used in the approach is the fact that the neighborhood properties for each of the minutiae points remains same in fingerprint images.

One of those characteristics is pair wise distance histogram that we have used in this algorithm.


Introduction Fingerprints are most useful biometric

feature in our body. Due to their durability, stability and

uniqueness fingerprints are considered the best passwords.

In places of access security, higher degree authentication, and restricted entry places fingerprints suggests easy and cheap method.


Biometric Modalities


Market Capture by different Biometric modalities


Previous Work Fingerprint Identification is quiet mature area of

research. Its almost impossible to describe all the previous approaches in a short time here.

The previous methods closely related to this approach are by Park et al.[2005] and Wamelen et al.[2000].

Park et al. from used pair wise distances first ever to match fingerprints in their approach before two years.

Wamelen et al. gave the concept of matching in two steps, Local match and Global match.


Different Features in a FingerprintRidge Ending

Enclosure

Bifurcation

Island

Texture

Singular points


High level description of algorithms in FVC 2004


Extraction of minutiae

Image skeleton

Gray scale image

Minutia features

Feature Extraction with CUBS-2005 algorithm

Developed by SHARAT et al


Problem Simulation The input fingerprint of the

same finger seems to be different while taken on different times.

There may be some translational, rotational or scaling changes, depending upon situation.

Our aim is to calculate these changes as a composite transformation parameter “T”.

The verification is done after transforming the input with these parameters, new transformed pattern should satisfy desired degree closeness with template pattern.

Template Input Pattern

cos sin( )

sin cosq x p

q y p

x t xs sq T p

y t ys s


Minutiae matching-Aligning two point sets

Input

Template


Process Steps The algorithm runs in two main steps:- (i) Local matching (ii) Global matchingIn local matching three stepwise calculations are there:1. Calculate “k” nearest neighbors for each and every

point in both patterns.2. Calculate histogram of pair wise distances in the

neighborhood of every point.3. Find out the average histogram difference between

all the possible cases.4. Set the threshold level of average histogram

difference5. Compare the average histogram differences with the

threshold level.


Flow Chart

No

No

Point pattern “P” stored in database pattern “Q” is taken that is to be matched with “P”

Select a local point and it’s “k” nearest neighbors in patterns P

Select a local point and it’s “k” nearest neighbors in patterns Q

Make pair wise distance Histogram

Average histogram difference < Threshold level

Calculate and store transformation parameter

Iteration algorithm to calculate final Transformation Parameter

Start

All point’s in pattern p is examined

End


Calculation of “k” Nearest neighbors (Local match)

For the given input fingerprint pattern and the template pattern, calculate “k” nearest neighbors in order to distances.

Here k is a constant can be calculated with the formula given by wamelen et al.(2000)


Histogram Calculation (Local Match) Histogram of pair wise distances in

their neighborhood for each and every point is calculated here. It describe the variety of distances of particular point in its neighborhood.

Here for one point “P1”; P1n1,P1n2,P1n3,P1n4,P1n5 are five nearest neighbors.

Note: step size is 0.04unit, here.

P1n2

P1

P1n1

P1n2

P1n3

P1n4

P1n5

P1n1P1n3

P1n4

P1n5

P1n2

P1n3

P1n4

P1n5

P1n3P1n4

P1n5

P1n4 P1n5


Average Histogram Difference and Threshold Setting (Local Match)

To calculate average histogram differences for two points, first subtract the their histograms. It comes in a form of matrix. To calculate average, just normalize it on corresponding scale.

H1=[4 2 0 0 2 0 1 2 3 1] H2=[1 3 2 5 0 0 2 3 0 1]

H1 - H2 =[3 -1 -2 -5 2 0 -1 -1 3 0]

Average histogram diff.(ΔH avg)=(1/10)*Σ(| H1 - H2 |i)

Setting of threshold depends on the size of point pattern. Larger the number of points, smaller the threshold count.


Transformation Parameter calculation

On the basis of histogram differences, we can make decision on their local matching. Then the transformation parameter is calculated in the following way. Here “r” represents the corresponding Transformation Parameter.

0

010

0

A xA yA xB

A yA xA yB

xA yA A B

yA xA A B

l

lr

l lD

l l

1i

k

xA ai

x

1

i

k

xB bi

x

1i

k

yA ai

y

1

i

k

yB bi

y

1

i i i i

k

A B a b a bi

l x x y y

1

i i i i

k

A B a b a bi

l x y y x

2 2

1i i

k

A a ai

l x y

2 2A xA yAD ll

cos sinT

x yr t t s s


Axial Representation of all Transformation Parameters after Local match

Three axes represent the translational (in both x& y direction), rotaional and scale changes.

The most dense part in graph represents the correct transformation parameters only. We need to conclude our results to that part.

-100-50

050

100

1.5

2

2.5

32

2.5

3

3.5

4

trans

all iterations together 5

rotangle

scal

e

-100

-80

-60

-40

-20

0

20

40

60

80

100

1.5

2

2.5

3

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

trans


Iteration Algorithm Iteration method is used to

converge the results towards dense part of the graph.

For applying this method we need to calculate mean and standard deviation of the distribution.

In graph all the transformation parameters are present, calculated after local matching step.

The mean for this distribution is presented by the “triangle” in centre.


Result after first iteration In the graph, black

star is describing the mean for the distribution.

After one iteration step some of the false transformation due to false local match has removed.


Result after second iteration After second

iteration, graph converges more towards the dense area.

Black star is the mean point for this distribution shown here.


After third iteration

Performing iterations to converge the result, gives the distribution having least standard deviation.

Black star in this graph is the desired transformation parameter i.e. “r”

cos sinT

x yr t t s s


Verification by transforming the template with calculated parameters

Template Pattern in Database Transformed version with Parameter ”r” Verification by Overlapping

Transformed With “r”


Experimental Results (With random generated data)

s. no. Total no of point taken

No of missing points

No. of matched points when no error added

No. of matched points when 2% error added


1 30 0 30 30 30

2 30 5 25 24 17

3 30 10 20 18 14

4 30 15 4 3 1

5 40 0 40 38 32

6 40 5 35 33 26

7 40 10 30 28 20

8 40 15 25 21 19

9 50 0 50 47 43

10 50 5 45 42 39


Results contd…

s. no. Total no of point taken

No of missing points

No. of matched points when no error added



11 50 10 40 36 25

12 50 15 35 28 22

13 50 20 30 25 20

14 60 0 60 59 47

15 60 5 55 36 31

16 60 10 50 29 20

17 60 15 45 40 36

18 60 20 40 30 25


Results with real and random fingerprints

The algorithm was tested on both randomly generated point pattern and real data base.

The results shows the correct identification in more than 98.87% cases with random data.

For real fingerprints we tested the method on some FVC 2004 samples. In most of cases performance was found satisfactory.


Limitations This algorithm is very much

dependent on accuracy of feature extraction method used for minutiae determination.

If feature extraction is not done correct then, results can have errors or false acceptances. Which effects performances and accuracy a lot.


Future work

This method is still dependent to some other algorithm for feature extraction.

First task is to develop a method for feature extraction from fingerprints.

Other thing to enhance is, its performance mainly on real fingerprint data.


Advantages of the Method over others Proposed earlier This algorithm undergoes two steps, so accuracy is

good and false acceptance rate is low. Calculation is less complex with comparison to others,

as histogram is a basis to select the local matching. While in other Random algorithms a lot of calculation is done for local match coz lacking in any basic attribute.

Algorithm doesn’t need very complex system to be implemented for practical/commercial purpose. So compatible to most of systems in use already.


Shortcomings Dependency on an efficient feature

extraction scheme. Histogram differences should be

calculated in a better way (Still to think over that…).


Thanks for your kind attention.

Fingerprint Minutiae Matching Algorithm using Distance Histogram of Neighborhood Presented By: Neeraj Sharma M.S. student, Dongseo University, Pusan South.

Documents

cgiv 19sep07

matching of minutiae

minutiae matching

fingerprint minutiae

template input pattern

local matching

input fingerprint

global matching