Fingerprint Registration

7/28/2019 Fingerprint Registration

1/22

FOCAL POINT LOCALIZATIONALGORITHM FORFINGERPRINT REGISTRATION

BY

NEMISHA KHOSA

85008


2/22

CONTENETS

INTRODUCTION

PREVIOUS ALGORITHMS

FOCAL POINT LOCALIZATION

ALGORITHM

CONCLUSION


3/22

INTRODUCTION

Biometrics is the Science and

Technology of Measuring and

Analyzing Biological data.


4/22

It is an Automated Method of recognizing a person based on aPhysiological orBehavioral characteristics

The Features measured are, Face

finger print

hand geometry

hand writing

Voice

Iris blood vessels in the retina

coloration in the cornea of the eye

DNA from tissue samples


5/22

FINGERPRINT REGISTRATION fingerprints are the traces of an impression from the

friction ridges of any part of a human hand

fingerprint registration are required to perform real-

time large-scale automatic fingerprint identification


6/22

PREVIOUS ALGORITHMS Pattern- Based

Poincare by Kawagoe and Tojo

Complex symmetrical filters, by Nilsson andBigun.

Projection- Based

hierarchical analysis of the orientation

coherence by Jing etc

local axial symmetry fields by Liu etc


7/22

FOCAL POINT LOCALIZATIONALGORITHM The proposed algorithm composes of 4 processes:

Pre-processing

Crossing-points localization

Initial block localization

Focal point localization

Focal point Quality and Assessment


8/22

PRE-PROCESSING Composes of fingerprint partitioning and directional

field estimation

Original fingerprint partitioned into 1616 blocks

Bazen and Gerezs approach


9/22

CROSSING POINTS LOCALIZATION The crossing point is defined as an intersection of two

straight lines which is perpendicular to two

orientation fields

The following equation is used to get the straight line:


10/22

Assume that two straight-line equations, l1and l2 ,

are

The crossing point that we can get from the two line(

l1, l2)


11/22

from (4) and (5), m2m1 cannot be zero i.e. the two

lines cannot be parallel.

If some line is parallel or almost parallel to the

other, the crossing point will be located very far

away or outside a fingerprint image.

only the crossing points in this effective area will

be employed in calculating the focal point.

Crossing

point


12/22

INITIAL BLOCK LOCALIZATION The block which contains a maximum number of

crossing points is the initial block.

The centre of this block is a starting point for the

next process.

Initial

block


13/22

FOCAL POINT LOCALIZATION ALGORITHM

START

Set centre of initial block to a centroid

Find Effective Area by the Centroid

Find the New Centroid

Focal Point =Centroid

Measure Centroid Shift

END

Convergence

?


14/22

THE FOCAL POINT LOCALIZATIONALGORITHM Step 1:Set iteration time to zero (i = 1), and set the

centre of the initial block to be a centroid (xct

(0),yct(0)).

Step 2:Select a top-half circle area. The top-half

circle area can be defined with radiusR (blocks) asshown in Fig

Step 2 The top-half circle

area with initial block as the

centre


15/22

Step 3:Generate the crossing points by using only

orientation fields in this top-half circle area, and find

the new centroid with the help of the equation:

where (xp(j),yp(j)) is the jth crossing point generated

by two lines which are perpendicular to two

orientation fields in thetop-half circle area.


16/22

Step 4: Find the (i)th shifted distance, or (i), between the

oldcentroid point (xct(i1),yct(i1)) and the new centroid

point(xct(i),yct(i)), given by


17/22

Step 5 : Check convergence or divergence condition ofthis centroid.

1. related to focal point convergence

if (distance) (i) (threshold)T

then iteration stops and this centroid is defined as the focal point.

2. related to focal point divergence

if (cumulative shifted distance) (i) (threshold) T

then iteration stops and the focal point is diverged

3. Else the new centroid (xct(i)

,yct(i)

) is replaced by the previousone,(xct(i1),yct(i1)), and repeat the step 2 through step 5 again

until the iteration ends.


18/22

FOCAL POINT QUALITY ANDASSESSMENT The number of crossing points, which contributed to

the stability of the detected focal point, can be used as

focal point quality parameter

The one, which obtained the highest number of the

crossing points, should be selected as the final focal

point.


19/22

ERROR MEASURMENT results in higher accumulation error than the other

techniques

Manually select 3 minutiae points

linearly project the detected focal point of afingerprint into another fingerprint with less error as

possible.

measure a distance error (DE) between 2 detected

focal points from two different impressions of the

same finger.


20/22

CONCLUSION The focal point is shown to be a very stable point for

fingerprint registration.

Moreover, the algorithm also consumed only 1/7 executiontime compared to the previous scheme (88.6 millisecond).

Future research also exploited the focal point applications in

fingerprint classification and fingerprint recognition.


21/22


22/22

REFERENCE

Vutipong Areekul and Natthawat Boonchaiseree , Fast Focal Point

Localization Algorithm for Fingerprint Registration , IEEE, Kasetsart

University Thailand, Page no 2089-2094

Fingerprint Recognition : Image Processing and Computer Vision By Vinay

Gupta & Rohit Singh

Google

Wikipedia

ACM Digital Library

Papers by Vutipong Areekul, Kittiwat Suppasriwasuseth, Suksan Jirachawang

A.M. Bazen and S.H. Gerez, Systematic methods for the computation of the

directional fields and singular points of fingerprints, IEEE Trans. Pattern

Anal. Machine Intell., vol.24, pp. 905-919, July 2002

Fingerprint Registration

Documents

Transcript of Fingerprint Registration