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
alia.qazzaz@uokufa.edu.iq
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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 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)









otherwise
V
I i j M
o
v
o
M
if I i j M
V
I i j M
o
v
o
M
N i j
( ( , ) )2
( , )
( ( , ) )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

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The orientation value in each pixel will be calculated by
using the following equations
( , ) ( 2 ) ( 2 ) 7 8 9 1 2 3 y p q  z  z  z  z  z  z (2)
( , ) ( 2 ) ( 2 ) 3 6 9 1 4 7 x p q  z  z  z  z  z  z (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
v i j p q p q y
w
i
w
p i
w
j
w
q j
y x     

 

 
(4)
( , ) ( , ) ( , ) 2
2
2
2
2
2 v i j p q p q y
w
i
w
p i
w
j
w
q j
x x    

 

 
(5)
( , )
( , )
tan
2
1
( , ) 1
v i j
v i j
i j
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
image i j
w w
bloack mean (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
cos(2 fx )
2
1
( , , , ) 2 1
2
1
2
2
1 






  

 





x y
x y
G x y f Exp
 
 (8)
cos sin 1 X  x  y (9)
sin cos 1 Y  x  y (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

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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.

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 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.

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