Gabor Filter

ISSN: 2312-7694

Ali et al. / International Journal of Computer and Communication System Engineering (IJCCSE)

92 | P a g e © 2014, IJCCSE All Rights Reserved Vol. 1 No.03 October 2014 www.ijccse.com

Gabor Filter

Ali Abdul Azeez Mohammad baker

Computer Science Department

Kufa university

Najaf/Iraq

[email protected]

Abstract—Gabor filter is a powerful way to enhance biometric

images like fingerprint images in order to extract correct features

from these images, Gabor filter used in extracting features

directly asin iris images, and sometimes Gabor filter has been

used for texture analysis. In fingerprint images The even

symmetric Gabor filter is contextual filter or multi-resolution

filter will be used to enhance fingerprint imageby filling small

gaps (low-pass effect) in the direction of the ridge (black regions)

and to increase the discrimination between ridge and valley

(black and white regions) in the direction, orthogonal to the ridge,

the proposed method in applying Gabor filter on fingerprint

images depending on translated fingerprint image into binary

image after applying some simple enhancing methods to partially

overcome time consuming problem of the Gabor filter.

Index Terms—Gabor filter, fingerprint, binary image, biometrics,

orientation.

I. INTRODUCTION

Every person own ten unique fingerprints. This makes

fingerprint matching system one of the most reliablesystems

for identifying people, fingerprint image may be shown as a

uniform pattern of parallel ridges and valleys run together,

ridges are the black regions while valleys are the white regions

in fingerprint image as illustrated in figure (1).some permanent

(like ridge ending and bifurcate) and semi-permanent features

such as scars, cuts are also shown in a fingerprint image.

There are many features can be discoveredin fingerprint

image which enable fingerprint matching system to make

sound judgment about whether any two prints came from same

finger or not, these features can be divided into two groups

Local features : A local feature consists of several

components, each component typically derived

from a spatially restricted region of the fingerprint ,

these features extracted from ridges by analyzing

the ridge behavior as individual or the relations

between consecutive ridges this group of features

involves many features, some of these features are

Ridge ending, bifurcation, Dot or island, Hook,

Lake, and Bridge, These features also called

minutiae and most fingerprint identification systems

depend only on only ridge ending and bifurcate in

matching process as illustrated in figure(1), these

features used in matching any two prints and enable

system in making decision if these two prints

identical or not. There are about (70 to 150)

minutiae in a typical fingerprint image.

Global features: these features involved two

important features which are core and delta ,core

can be defined as the top most point on the inner

most ridge while delta point can be defined as the

point where three ridge directions meet as

illustrated in figure(1), these features also called

singular points or singularities.

Fig. 1 fingerprint image

To extractglobal features precisely, fingerprint image must

be enhanced by using perfect methods of contextual filter or

multi-resolution filter, and if the enhancement step uses a

single filter convolution for the entire fingerprint image, it

creates significant number of false minutiae, a large number of

true minutiae are missed and, a significant error in the location

(position and orientation) of minutiae may be introduced.

II. PROPOSED METHOD

The proposed system consist of the following steps as

illustrated in figure (2)

Applying median filter.

Normalization.

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ISSN: 2312-7694



Calculating pixels orientation by using Sobel image.

Dividing image into blocks, and calculating blocks

orientation.

Translatingfingerprint image into binary.

CalculatingGabor filter for each pixel.

Each one of the above steps can be illustrated as follows

A. Applying median filter

The fingerprint image divided into (3×3) matrices, each

matrix translated into a victor with (9) values that arranged in

any order(ascending or descending)then the center of the

matrix will be replaced with the middle value of the vector, the

result of applying this filter can be illustrated in figure (3).

Fig. 2 block diagram of the proposed system

B. Normalization process

Normalization process is used to fixed the intensity values of

the pixels within a desired or wanted range by applying

equation (1)

otherwiseV

MjiIo

v

oM

MjiIifV

MjiIo

v

oM

jiN2)),((

),(

2)),((

),(

Where, M and V are the mean and variance of the fingerprint

image I (i, j), Mo and Vo are the desired mean and variance

values.

The result of applying this process is illustrated in figure (3)

a .Original image b. applying median filter

c. normalization result

Fig. 3 Applying median filter and normalization process

C. Applying Sobel masks

Orientation in each pixel can be calculated by using Sobel

vertical and horizontal masks as illustrated in figure (4)

Z1 Z2 Z3

-1 -2 -1

-1 0 1

Z4 Z5 Z6 0 0 0 -2 0 2

Z7 Z8 Z9 1 2 1 -1 0 1

a- Image b- Vertical mask c- Horizontal mask

Fig. 4 Sobel masks

Original image

Applying Sobel masks to

calculate orientation for each

pixel

Normalization

Applying median filter

Dividing fingerprint image into blocks and

Calculating blocks orientation.

Constructing and applying Gabor filter for each pixel in

binary fingerprint image

Translating to

binary image

ISSN: 2312-7694



The orientation value in each pixel will be calculated by

using the following equations

)2()2(),( 321987 zzzzzzqpy (2)

)2()2(),( 741963 zzzzzzqpx (3)

D. Dividing image into blocks, and calculating blocks

orientation

The fingerprint image will be divided into non overlap

blocks with size of (W×W) , and the orientation of each block

will be calculated as follows

),(),(2),(2

2

2

2

qpqpjiv y

wi

wip

wj

wjq

xy

(4)

),(),(),( 22

2

2

2

2 qpqpjiv y

wi

wip

wj

wjq

xx

(5)

),(

),(tan

2

1),( 1

jiv

jivji

x

y (6)

Where θ is The block orientation and (w =17)

E. Translating fingerprint image into binary image

The fingerprint image will be converted into a binary

representation as shown in figure (5) by dividing the image

into (W×W) non overlap blocks and calculating the mean for

each block by using equation (7)

1

0

1

0

),(1 w

i

w

j

jiimageww

meanbloack (7)

Binary image (i, j) =255

if enhanced image pixel (i, j) ≥ block mean

Binary image (i, j) =0

if enhanced image pixel (i, j) < block mean

a-original image b- enhanced image

c- binary image

Fig. 5 Binary image

F. Calculating Gabor filter for each pixel

The fingerprint image will be divided into (W × W) overlap

blocks and these blocks will be filtered with Gabor filter. An

even symmetric Gabor filter has the following general form in

the spatial domain

)fx2cos(2

1),,,( 12

2

1

2

2

1

yx

yxExpfyxG

(8)

sincos1 yxX (9)

cossin1 yxY (10)

Where, (ƒ) is the frequency of the sinusoidal plane wave

along the direction (θ) from the x-axis, and (δx, δy) are the

space constants of the Gaussian envelope along x and y axes,

respectively. In our proposed method we used ƒ =0.1,

δx=4,and δy=4, The result of applying Gabor filter is illustrate

in figure (6).

a- Original image b- Image after apply Gabor filter

Fig. 6 Applying Gabor filter

ISSN: 2312-7694



III. RESULTS

After applying the proposed method on fingerprint images

the results of three examples will be illustrated

Example 1:-

a-original image b-enhanced image

c-binary image d-Gabor image

Fig. 7 results (1)

Example 2:-



Fig. 8 results (2)

Example 3:-



Fig. 9 results (3)

IV. CONCLUSION

Applying Gabor filter on binary image simplified

calculation and makes perfect enhanced results.

Multi resolution filters are time consuming compared

with simple filters.

ISSN: 2312-7694



Good enhancement methods make fingerprint

system more reliable.

REFERENCES

1- [Iwasokun 2012] Iwasokun Gabriel

Babatunde, AkinyokunOluwole Charles, Alese

Boniface Kayode, and OlabodeOlatubosun

"Fingerprint Image Enhancement: Segmentation to

Thinning",(IJACSA) International Journal of

Advanced Computer Science and Applications,

2012.

2- [Kumud 2011] KumudArora, and

Dr.PoonamGarg "A Quantitative Survey of

various Fingerprint Enhancement techniques",

International Journal of Computer Applications,

2011.

3- [Liu 2008] Liu Wei "Fingerprint

Classification Using Singularities Detection",

international journal of mathematics and computers

in simulation, 2008.

4- [Peihao 2007] Peihao Huang, Chia-Yung

Chang, Chaur-Chin Chen "Implementation of an

Automatic Fingerprint Identification System",

IEEE, 2007.

5- [Salil 2002] Salil Prabhakar, Anil K. Jain,

and Sharath Pankanti "Learning fingerprint

minutiae location and type", Watson Research

Center, Yorktown Heights, NY 10598, USA, 2002.

6- [William 2001] William K. Pratt "digital

image processing ", Los Altos, California, USA,

2001.

Technology

Gabor Filter