Fingerprint Recognition Using CorrelationLalithkrishnan H

Gautham s

EEE department, R.M.K engineering college –kavaraipettai.

lalithkrishnanh@gmail.com

s_gautham1@rediffmail.com

Keywords: optical fingerprint identification, biometrics, artificial neural network, optical

correlation

Abstractn.:A major approach for

fingerprint recognition today is to

extract minutia features from

fingerprints and to perform print

matching based on minutia

parirings. One of the most difficult

problem in the fingerprint

recognition have been that the

recognition performance, which may

vary depending on environmental or

personal causes, this paper discusses

the hybrid system based on optical

preprocessor and artificial neural

network.

INTRODUCTION

Biometric identification

has been receiving extensive attention

over the past decade with increasing

demands in automated personal

identification. Biometrics is to identify

individuals using physical or behavioral

characteristics. Physical biometrics is

based on the external characteristics of an

individual, which is unique. Behavioral

biometrics is based on the characteristics

of an individual at that instant. This may

change over a period.

However it has been known

that there are number of people whose

fingerprints could not be identified by the

feature based methods due to special skin

condition, where feature points are hard to

be extracted by image processing. Ratio of

people having this problem varies

depending on sex, age, job grouping, etc.

Addressing this problem, the

paper discusses the capabilities of hybrid

system based on optical wavelet

processing. Most digital processing

methods for fingerprint recognition are

based on extraction of minutia features.

The advantage of this method is to

identify based on the image processing

method instead of minutia features. The

major Minutia features of fingerprint

ridges are: ridge ending, bifurcation,

and short ridge (or dot). The ridge

ending is the point at which a ridge

terminates. Bifurcations are points at

which a single ridge splits into two

ridges. Short ridges (or dots) are ridges

which are significantly shorter than the

average ridge length on the fingerprint.

Minutiae and patterns are very

important in the analysis of

fingerprints since no two fingers have

been shown to be identical.

The three basic

patterns of fingerprint ridges are the

arch, loop, and whorl. An arch is a

pattern where the ridges enter from one

side of the finger, rise in the center

forming an arc, and then exit the other

side of the finger. The loop is a pattern

where the ridges enter from one side of

a finger, form a curve, and tend to exit

from the same side they enter. In the

whorl pattern, ridges form circularly

around a central point on the finger.

fig.1 Ridge ending

fig 2 Bifurcation

CORRELATION

Correlation is a

statistical technique that can show

whether and how strongly pairs of

variables are related. For example, height

and weight are related; taller people tend

to be heavier than shorter people are.

CALCULATING CORRELATION

Now we're ready to compute the

correlation value. The formula for the

Value of

r (or rs)

Interpretation

1.0 Perfect correlation

0 to 1 The two variables tend to

increase or decrease

together.

0.0 The two variables do not

vary together at all.

correlation is:

We use the symbol r to

stand for the correlation.

CORRELATION COEFFICIENT

The correlation

coefficient, r, ranges from -1 to +1.

The nonparametric Spearman

correlation coefficient, abbreviated

rs, has the same range.

OPTICAL WAVELET

PROCESSOR:

An optical correlator

is a device for comparing two signals

by utilising the Fourier transforming

properties of a lens. It is commonly

used in optics for target tracking and

identification.The correlator has an

input signal which is multiplied by some

filter.

In the Fourier

domain.whenever an image is incident

on the convex lens it is produce an exact

image having same fourier transform.It

stores its module using a computer and

this is compared with a sample.when

both the samples superimpose a planar

wave is created and recognition signal is

produced.

The optical processor

receive the reflected beam of laser from

the print and feds it to the rocesssing

system.The system to which it is fed can

be both within the computer or isolated

from the circuit. Normally for removing

the high frequency distortions.

Fig 3.BLOCK DIAGRAM OF

WAVELET PROCESSING

Fingerprint Edges enhancement

Wavelet processing

ANN Module

3.1)Low Pass Filtering:

To get rid of

the numerous high-frequency spikes

that seem to be present in the original

images, we replace every pixel that

significantly deviates from the values

of its four neighbors by the

corresponding average. Filters do this.

3.2) Segmentation:.

For each

image, we first draw a tight rectangular

box around each fingerprint using an

edge detection algorithm and

determine the geometric center of the

box. The central region of the

reference image is then defined to be

the 65 x 65 central square patch that

occupies the region immediately below

the previously described center. For

the test image, instead we select a

similar but larger patch of size

105 x 105 (extending the previous

patch by 20 pixels in each direction).

This larger patch is termed the

window.

3.3) Alignment:.

We slide, pixel by

pixel, the central region of the

reference image across the window of

the test image (by 20 pixels up, down

left and right) and compute at each step

the corresponding correlation, until we

find the position where the correlation is

maximal. This, aside from the training

period, is the most computationally

expensive part of the entire algorithm.

The central region of the test image is

then determined by selecting the central

65 x 65 patch corresponding to the

position of maximal correlation

COMPRESSION AND

NORMALISATION

• Finally, each one of the two 65 x

65 central regions is reduced to a

32 x 32 array by discrete

convolution with a truncated

gauss Ian of size 5 x 5.

• This 32 x 32 compressed central

region contains a low-resolution

image, which corresponds

roughly to 10 ridges in the

original image.

• The resulting pixel values are

conveniently normalized between

0 and 1.

• In our implementation, all the

parameters and in particular the

sizes of the various rectangular

boxes are adjustable.

MAPPPING

After the mapping is done the

image is illuminated through light and

intensity of pixels at various points

(x,y) are noted and scatter plot is

plotted and from this the linearity is

studied.For example , Here F(xi ,yj) is

the pixel intensity or the gray scale

value at a point (xi ,yj) in the

undeformed image. G(xi* ,yj*) is the

gray scale value at a point (xi* ,yj*) in

the deformed image. and are mean

values of the intensity matrices F and

G, respectively. The coordinates or

grid points (xi ,yj) and (xi* ,yj

*) are

related by the deformation that occurs

between the two images. If the motion

is perpendicular to the optical axis of

the camera, then the relation between

(xi ,yj) and (xi* ,yj

*) can be

approximated .

Here u and v are

translations of the center of the sub-

image in the X and Y directions,

respectively. The distances from the

center of the sub-image to the point (x,

y) are denoted by Δx and Δy. Thus, the

correlation coefficient rij is a function

of displacement components (u, v) and

displacement gradients .

IMAGE CONVERSION

The major part in

this recognition is conversion of the

RGB format into gray shades. The

intensity of the gray shade denotes

whether the image is a bright coloured

image or light colored image. The gray

shade distribution is noted in a histogram

called color histogram.

fig 4. BEFORE CONVERSION

(GRAY FORMAT)

fig 5 AFTER CONVERSION (GRAY

FORMAT)

fig 6.HISTOGRAM PROCESSING

Onc

e the histogram is formed after

conversion, the scatter points are

noted. To improve the accuracy of the

recognition it is better to select points

towards the middle of the curve. These

points are stored as template points.

SCATTER PLOTS

Once the values are noted

for template and recognition purpose,

the values are plotted between

respective pixels. These are similar to

line graphs in that they use horizontal

and vertical axes to plot data points.

However, they have a very specific

purpose. Scatter plots show how much

one variable another affects. The

relationship between two variables is

called their correlation.

Scatter plots usually

consist of a large body of data. The

closer the data points come when plotted

to making a straight line, the higher the

correlation between the two variables, or

the stronger the relationship.

If the data points make a straight line

going from the origin out to high x- and

y-values, then the variables are said to

have a positive correlation. If the line

goes from a high-value on the y-axis

down to a high-value on the x-axis, the

variables have a negative correlation.

METHODOLOGY OF

RECOGNITION

Once the patterns are got by

optical scanning, the above-mentioned

processing techniques are performed and

for particular displacement the intensity,

values are noted. These values are stored

as template. The same procedure is

repeated for sample and for the same

displacement the intensity, values are

noted. For both the intensity values the

scatter plot is plotted. The correlation is

then done for the graph. If it is one then

sample matches .if it is –1 then the

sample does not match.

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5

i nt e ns i t y ( t e mpl a

t e )

intensity(template)

intensity(sample)

Fig 7. POSITIVE CORRELATION

0

10

20

30

40

50

60

1 2 3 4 5

IN TE NS ITY( TE MP L A TE )

INTIENSITY(TEMPLATE)

INTENSITY(SAMPLE)

Fig 8.NEGATIVE CORRELATION

ADVANTAGES

As a consequence

the following method is desirable:

• no contact with the specimen

required

• sufficient spatial resolution to

measure locally at the region of

interest

• the ability to capture non-uniform

full-field deformations

• a direct measurement that does

not require recourse to a

numerical or analytical model.

REFERENCES:

Ebooks

Correlation Pattern Recognition

BY: Kumar, B.V.K Vijaya.

(1)Biometrics for Network Security

By: Paul Reid ebook.

(2) “Biometrics recognition:

security and privacy concerns”

S.Prabhakar, S. Pankanti,

IEEE security magazine.

(3)“High-speed fingerprint

verification using an correlator”

A. Stoianov, C. Soutar