In Computer Vision, Segmentation Refers to the Process of Portioning a Image into Multiple Segments (Sets of Pixels).The goal of Segmentation is to Simplify and change the Representation of an Image into Something that is More Meaningful and Easier to Analyze. Fingerprint Segmentation is an Important Pre-Processing step in Automatic Fingerprint Identification System. The task  of  a fingerprint  Segmentation Algorithm is to Decide Which Part of the Image belongs to the fore-ground, Originating from the Contact of a fingertip With the Sensor, and Which Part to the Background , Which is the Noisy area at the border of the Image. We Study Different Algorithm for the Segmentation of fingerprints is Presented and SVM (support vector machine) Classifier for fingerprint Image Classification. Segmentation and Classification are the Important Concepts of Digital Image processing (DIP

Pandey, Anjani

Singh, Gayatri

English

Binary Journal of Data Mining & Networking

  
 
 Binary Journal of Data Mining & Networking 4 (2014) 33-36 
http://www.arjournals.org/index.php/bjdmn/index 
 
Research Article 
             
Survey of Different Segmentation Method for Low Quality Fingerprint Image   
Anjani Pandey1 , Gayatri Singh 
 
*Corresponding author: 
 
Anjani Pandey 
 
1Dept of Computer Engineering V.I.T.S, 
Satna (M.P) 
2Dept of CSE (Software System) 
V.I.T.S, Satna (M.P) 
 
 
 
 
A b s t r a c t  
In Computer Vision, Segmentation Refers to the Process of Portioning a Image into Multiple 
Segments (Sets of Pixels).The goal of Segmentation is to Simplify and change the Representation 
of an Image into Something that is More Meaningful and Easier to Analyze. Fingerprint 
Segmentation is an Important Pre-Processing step in Automatic Fingerprint Identification System. 
The task  of  a fingerprint  Segmentation Algorithm is to Decide Which Part of the Image belongs to 
the fore-ground, Originating from the Contact of a fingertip With the Sensor, and Which Part to the 
Background , Which is the Noisy area at the border of the Image. 
We Study Different Algorithm for the Segmentation of fingerprints is Presented and SVM (support 
vector machine) Classifier for fingerprint Image Classification. Segmentation and Classification are 
the Important Concepts of Digital Image processing (DIP) 
Keywords: Segmentation, Classification, DIP, Sensor, SVM. 
Introduction 
In an increasingly digitized world the reliable personal 
authentication has become an important human computer interface 
activity. National security, e-commerce and access to computer 
networks are now very common where establishing a personÊs 
identity has become vital. Existing security measures rely on 
knowledge-based approaches like passwords or token-based 
approaches such as swipe cards and passports to control access 
to physical and virtual spaces, but there methods are not very 
secure. Tokens such as badges and access cards may be 
duplicated or stolen. Passwords and personal identification number 
(PIN) numbers may be stolen electronically. Furthermore, they 
cannot differentiate between authorized user and a person having 
access to the tokens or knowledge. Biometrics such as fingerprint, 
face and voice print offers means of reliable personal 
authentication that can address these problems and is gaining 
citizen and government acceptance. 
Fingerprint 
Fingerprint is fully created at about seven months of fetes 
development and it is unique and unchangeable during individualÊs 
cuts or injuries. Fingerprint consists of a sequence of ridges and 
valleys on the surface of the finger. In image point of view, ridges 
are actually the dark part in the image except the presence of 
different kind of noise, whereas valleys are the bright part. 
Frequently, ridges and valleys go in parallel but sometimes they 
bifurcate or terminate. Bifurcation is present when the ridge 
suddenly split into two ridges and termination exists when t fact, 
bifurcation and termination represent some of important minutiae 
points.  
Different segmentation techniques of fingerprint image 
New Modified Gradient Based Technique 
The objective of Fingerprint segmentation is to extract the region of 
Interest(ROI) which contains the Desired fingerprint Impression. 
Present a Modified Gradient Based Method to Extract ROI. The 
distinct feature of these technique is that it gives high Accurate 
Segmentation percentage for fingerprint Image Even in case of low 
quality fingerprint Images. 
Mean And Variance Based Method  
Mean and Variance based method can Significantly Reduce the 
number of basic image entities. And due to the good discontinuity 
preserving filtering characteristic, the salient features of the overall 
image are retained [1]. 
Steps for mean and variance based method [1] are summarized as 
follows 
1.Divide the input image I (I,j) into non-overlapping blocks with size 
wxw. 
2. Compute the mean value M(I) for each block using equation 1. 
           
                 1      w/2         w/2 
      M(I)  ―       ∑             ∑ I(i,j)                        (1) 
               W2    i=w/2      j=w/2 
3.use the mean value computed  in step 2 to compute  the 
standard deviation value std(I) from equation 2. 
 
 
 
 
ISSN: 2229 -7170
  
This work is licensed under a Creative Commons Attribution 3.0 License.  
 Pandey et al. Binary Journal of Data Mining & Networking 4 (1) 33-36 [2014] 
 
PAGE | 34 |
 
 
                     1      w/2       w/2 
      Std(I)=   ―     ∑           ∑    (I(I,j)―M(I))2      (2) 
                    W2      i=w/2   j=-w/2                           
 
4.Select Empirically a Threshold Value Working on Different 
Images. If the (1) is Greater than Threshold Value, the Block is 
Consider as foreground otherwise it Belongs to Background. 
Direction Based Methods  
This Method is Mainly Based on the Coherence of Direction. A 
fingerprint Consists of Parallel  line Structures the Coherence will 
be Considerable higher in the foreground Than in the 
Background[4]  
The  Steps for Direction Based Method [4][5] are Summarized as 
Follows; 
1. Dived the Input image I(i,j) into Non- Overlapping Blocks With 
Size wxw. 
2.Use 3x3 Sobel Vertical and Horizontal Marks Defined in 
Equalization 3 and 4 Respectively to Compute the Gradient dx (I,j) 
and dy(I,j) And at Each Pixel(I,j) which is the Center of the Block. 
 
                               -1    0    1 
Sobel Vertical =              -2    0     1      (3)                                         
                                       -1    0     1  
 
 
 
 
                              -1   -2    1  
Sobel Horizontal =            0    0    0  (4)                
                                        1    2    1  
 
3. Estimate the local Orientation using equation 5,6 and 7 [6] 
 
                                    i+w/2            j+w/2 
 Vx(i,j)  =    ∑                   ∑  (dx(u,v))(dy(u,v))      (5)      
                  U=i-w/2    v=j-w/2 
 
 
                    i+w/2            j+w/2 
 Vy(i,j) =   ∑                   ∑   dx2(u,v) - dy2(u,v)      (6)     
                   U=i-w/2        v=j-w/2 
 
                      i+w/2            j+w/2 
 Vz(i,j) =      ∑                   ∑ (dx(u,v) +dy(u,v))2     (7)     
                        U=i-w/2        v=j-w/2 
 
 
4. Calculate Background Certainly and Orientation field using 
equation 8. 
                   
      coh  =     (Vx2  (I,j) +Vy2  (I,j))           (8)   
                                w2*Vz 
 
5. select empirically a threshold value working on different images. 
If the coherence is greater than threshold value, the block is 
considered as foreground otherwise it belongs to background. 
In linear Hybrid Classifier for fingerprint Segmentation 
Method  
 A Hybrid algorithm based on linear classifiers for the segmentation 
of fingerprints is presented. The propose algorithm uses a Block-
wise classifier to separate foreground and background blocks in 
the main and employ a pixel wise classifier to deal with pixels 
accurately. In order to evaluate the performance of the new 
methods based on other classifiers. 
A captured fingerprint image usually consists of two components, 
which one is called the foreground and the background [2] .the 
foreground is the component that originated from the contact of a 
fingertip with the sensor. the noisy area at the borders of the image 
is called the background[3] 
Block-wise segmentation 
Block-wise segmentation includes foreground and background 
blocks to reduce computational consumption in subsequent 
procedure and avoid introducing false segmentation within small 
region. The fingerprint image portioned into blocks of 16*16 pixels. 
Two features block mean information and block variance 
information, are used in block-wise segmentation. For most 
fingerprint sensors, the ridge-valley structures can be 
approximated by black and white lines, while the background, 
where the finger does not touch the sensor is rather white. This 
means that the mean gray value in the foreground is in general 
lower, that is, darker gray, than it is in the background. 
 
                          1        15     15 
Mean(m,n)=   ―         ∑       ∑    I(i+16m,j+16n) 
                     16?16    i=0    j=0 
 
In general, the variance of the ridge-valley structures in the 
foreground is higher than the variance of the noise in the 
background. The block variance for each block is given by:   
 
                                 1       15     15 
   Variance (m,n)=   ―       ∑      ∑{I(i+16m,j+16n) ? mean(m,n)}2 
                              16?16   i=0   j=0 
 
 Each of blocks is assigned into three sets, foreground, background 
and pending blocks set, respectively. Using class w1 for the 
foreground block, class w0 for the background block, class w2 for 
the pending block, and w for the assigned class, the following 
decision function is applied; 
 
ω0   mean(m, n)< T1 , variance(m, n)<T2 
ω= ω1 mean(m, n)>T3, variance(m, n)>T4 
ω2 otherwise Where T1,T2,T3 and T4 are threshold values. 
 Pandey et al. Binary Journal of Data Mining & Networking 4 (1) 33-36 [2014] 
 
PAGE | 35 |
 
 
Pixel-wise segmentation 
Pixel-wise segmentation identifies and removes non-ridge pixels 
from the pending blocks. In this procedure, Coherence, Mean, and 
variance and selected as the pixel features. The coherence gives a 
measure how well the gradients are pointing in the same direction . 
In a window W around a pixel, the coherence is defined as 
 
                l ∑w(Gs,x,Gs,y)l          (Gxx-Gyy)2+4G2xy 
Coh=                                =  
                 ∑wl(Gs,x,Gs,y)l             Gxx+Gyy 
 
Where  (Gs,x,Gs,y) is the squared gradient, 
Gxx = ∑G2x      ,    Gyy =∑G2y       , Gxy =  ∑Gx Gy 
          W                     w                     w 
And (GX,GY) is the local gradient. 
           
The average gray value measures how gray the pixel is. Using   I  
as the intensity of the image, the local mean for each pixel is given 
by; 
 
Mean=∑ I 
                     W 
 
 The variance measures the gray variance around the local area. 
The variance is for each pixel is given by; 
 
Var =    ∑  (1-Mean )2  
                       W 
 
The Coherence, Mean and variance feature values are normalized 
in the [0, 1] range. The proposed pixel-wise segmentation is based 
on [8].  The algorithm presented in [8] uses a linear classifier which 
is called label box to enhance the performance of fingerprint image 
segmentation. The classifier uses three pixel features, being the 
coherence, the mean and the variance. The parameter modal is 
established using sample space at first. Then the type(background 
and foreground) of each pixel is pending blocks is decided by the 
parameters model and features. 
Low Quality Fingerprint Images 
This is a new algorithm to segment fingerprint Images [9]. These 
algorithm uses four features the global mean, the local mean, 
variance and coherence of the image to achieve the fingerprint 
segmentation. Based on these features a rule features a rule 
based system is built to segment the Image. 
The proposed algorithm is implemented in three stages; pre ?
processing, segmentation and post processing. Gaussian filter and 
histogram equalization are applied in the pre-processing stage. 
Segmentation is applied using the local features. Finally, fill the 
gaps algorithm and a modified version of threshold in the post-
processing stage. Segmentation stage. 
Feature for fingerprint segmentation     
Fingerprint features must reflect both the gray level of fingerprint 
and the direction of ridge lines . however the complicated 
construction of fingerprint  pattern  and the Imbalance  in the 
contrast, require local feature  Instead of the global features for 
gray-level based methods while coherence is the feature for 
direction based method . the combination for these features in one 
algorithm show efficiently the distribution of the pixels for ridges 
and valleys in the image. coherence feature indicates the strength 
of the local window gradients centered on the processed point 
along the same dominate  orientation. 
local mean, local variance and local coherence [7] are calculated 
as following  
 
                 1                               1 
mean =   ·   ∑ I   ,Variance =  ·  ∑  (I-mean)2  
               w2    w                      w2  w   
 
where I is Intensity and w is the window size Centered on the 
processed pixels. 
 
coh=           (gxx -gyy)2 +4(gxy)2 / (gxx+gyy) 
        
    gxx =∑Gx2 , gyy =∑ Gy2 ,gXy = ∑  Gx Gy 
                      w                 w                  w   
 
where Gx and  Gy are corresponding horizontal and vertical 
gradient  components which are given by sobel operators. 
   
   
 
Pre-Processing 
fingerprint image with low contrast, false traces ridges or noisy 
complex background cannot be segmented correctly. therefore it is 
required to enhance the image.  Gaussian filter used in this method 
and histogram equalization. Gaussian filter used to smooth the 
image and hence background areas. this step together with split 
and merge technique , with is applied in the next stage, will collect 
pixels with similar gray levels into big areas. histogram equalization 
is invoked in this stage too. when the global mean of the image 
under consideration is higher than a certain threshold, which mean 
a bright Image , histogram equalization is used to enhance the 
image by reduce the brightness of the image. 
Segmentation 
In this stage , Split and merge technique is applied to collect similar 
background areas after the smoothing process. in order to 
separate the foreground from the background, the image is divided 
into a number of non-overlapping sub ?images of size 10X10 pixels 
and the local mean, local variance and local coherence are 
computed for each sub-image. the global mean together with the 
three a fore mentioned parameters are used to build a rule based 
Pre -
processing
Segmentatio
n
Post-
processing
 Pandey et al. Binary Journal of Data Mining & Networking 4 (1) 33-36 [2014] 
 
PAGE | 36 |
 
 
system to segment the foreground and background of the image 
under 
consideration. the result of this rule based system is to decide 
whether a certain block is a foreground or a background. 
Post Processing 
the segmentation fingerprint image may contain isolated 
background blocks which are surrounded by foreground blocks. 
obviously these background blocks are foreground in the original 
image . a simple post processing technique is proposed to 
eliminate the presence of these isolated blocks. 
Support Vector Machine (SVM) 
The key idea of SVM is generalization: A classifier need not only to 
work well on the training samples, but also       to work equally well 
on previously unseen samples. Although this is realized long 
before the appearance of SVM,  it is SVM that gives this idea a 
good implementation.  A linear SVM  is a separation  hyper plane 
with its separation margin maximized and the number of  mis-
classified samples minimized. The margin is defined as the 
distance between the hyper plane and the samples of the two 
classes that are closest to the hyper plane (among those being 
correctly classified). By minimizing the number of training errors, 
SVM seeks good performance on the training data; by maximizing 
the margin, the generalization ability for future data is optimized.we 
are using the new technique  for Fingerprint Image Classification is 
Support Vector Machine fingerprint classification is Important for 
different practical applications.  an accurate and consistent 
classification can greatly reduce fingerprint matching time for large 
database. 
 
 
References 
[1]. Maltoni D, Maio D, Jain A, and 
Prabhakar S. Hand Book of fingerprint 
recognition, springer ,newyork,2003. 
[2]. Bazen AM,  and Gerez SH. 
„segmentation of fingerprint images‰, 
proc-pro RISC 2000, 12th Annual 
workshop on circuits , systems and 
signal processing ,Nov 2001. 
[3]. Chen XJ, Tian J, Cheng JG, and Yang 
X. „segmentation of fingerprint‰ 
[4]. Bazan AM,  and Gerez SH. ÂÊDirectional 
Fields Computation for Fingerprint 
Based on the Principal Component 
Analysis of local Gradient‰ proRISC 
2000 Workshop on Circuits, Systems 
and Signals Processing ,Veldhoven,the 
Netherlands,Nov.2000. 
[5]. Lim and Jac S. ÊÊTwo Dimensional  
Signal and Image Processing „, 
Englewood Cliffs, NJ,  Prentice Hall, 
PP.469-476,1990. 
[6]. Dario Maio and Davide Maltion , „Direct 
Gray-Scale Minutiae Detection in 
FingerprintsÊÊ ,IEEE Trans An Pattern 
Analysis and Machine Intelligence , 
Vol.19,PP.27-40,Jan.1997. 
[7]. Helfroush M, and Mohammad M. pour, 
ÊÊFingerprint Segmentation,ÊÊ  Presented 
at 3rd  International Conference ÂÊ on 
Information and Communication 
Technologies: From Theory to 
application, Damascus, Syria. 2008. 
[8]. Ren CX, Yin YL, and Ma J. „ 
Fingerprint image segmentation based 
on linear classifier‰, fifth international 
Symposnum on Multispectral Image 
Processing and Pattern Recognition 
(MIPPR 2007),Proceedings of the 
International Sociaty For Optical 
engineering (SPIE),Vol. 6790, 
PP.67905J, wwhan china, Nov. 15-17, 
2007. 
[9]. Hasan Fleyeh, Diala Jomaa, Mark 
DoughertyÂÊSegmentation of Low 
Quality Fingerprint ImagesÊÊ IEEE 
member Computer Science 
Department, Dalarna University, 
Borlänge ? Sweden-2009. 
 
 
 


Survey of Different Segmentation Method for Low Quality Fingerprint Image

Advanced Research Journals

  
 
 Binary Journal of Data Mining & Networking 4 (2014) 33-36 
http://www.arjournals.org/index.php/bjdmn/index 
 
Research Article 
             
Survey of Different Segmentation Method for Low Quality Fingerprint Image   
Anjani Pandey1 , Gayatri Singh 
 
*Corresponding author: 
 
Anjani Pandey 
 
1Dept of Computer Engineering V.I.T.S, 
Satna (M.P) 
2Dept of CSE (Software System) 
V.I.T.S, Satna (M.P) 
 
 
 
 
A b s t r a c t  
In Computer Vision, Segmentation Refers to the Process of Portioning a Image into Multiple 
Segments (Sets of Pixels).The goal of Segmentation is to Simplify and change the Representation 
of an Image into Something that is More Meaningful and Easier to Analyze. Fingerprint 
Segmentation is an Important Pre-Processing step in Automatic Fingerprint Identification System. 
The task  of  a fingerprint  Segmentation Algorithm is to Decide Which Part of the Image belongs to 
the fore-ground, Originating from the Contact of a fingertip With the Sensor, and Which Part to the 
Background , Which is the Noisy area at the border of the Image. 
We Study Different Algorithm for the Segmentation of fingerprints is Presented and SVM (support 
vector machine) Classifier for fingerprint Image Classification. Segmentation and Classification are 
the Important Concepts of Digital Image processing (DIP) 
Keywords: Segmentation, Classification, DIP, Sensor, SVM. 
Introduction 
In an increasingly digitized world the reliable personal 
authentication has become an important human computer interface 
activity. National security, e-commerce and access to computer 
networks are now very common where establishing a personÊs 
identity has become vital. Existing security measures rely on 
knowledge-based approaches like passwords or token-based 
approaches such as swipe cards and passports to control access 
to physical and virtual spaces, but there methods are not very 
secure. Tokens such as badges and access cards may be 
duplicated or stolen. Passwords and personal identification number 
(PIN) numbers may be stolen electronically. Furthermore, they 
cannot differentiate between authorized user and a person having 
access to the tokens or knowledge. Biometrics such as fingerprint, 
face and voice print offers means of reliable personal 
authentication that can address these problems and is gaining 
citizen and government acceptance. 
Fingerprint 
Fingerprint is fully created at about seven months of fetes 
development and it is unique and unchangeable during individualÊs 
cuts or injuries. Fingerprint consists of a sequence of ridges and 
valleys on the surface of the finger. In image point of view, ridges 
are actually the dark part in the image except the presence of 
different kind of noise, whereas valleys are the bright part. 
Frequently, ridges and valleys go in parallel but sometimes they 
bifurcate or terminate. Bifurcation is present when the ridge 
suddenly split into two ridges and termination exists when t fact, 
bifurcation and termination represent some of important minutiae 
points.  
Different segmentation techniques of fingerprint image 
New Modified Gradient Based Technique 
The objective of Fingerprint segmentation is to extract the region of 
Interest(ROI) which contains the Desired fingerprint Impression. 
Present a Modified Gradient Based Method to Extract ROI. The 
distinct feature of these technique is that it gives high Accurate 
Segmentation percentage for fingerprint Image Even in case of low 
quality fingerprint Images. 
Mean And Variance Based Method  
Mean and Variance based method can Significantly Reduce the 
number of basic image entities. And due to the good discontinuity 
preserving filtering characteristic, the salient features of the overall 
image are retained [1]. 
Steps for mean and variance based method [1] are summarized as 
follows 
1.Divide the input image I (I,j) into non-overlapping blocks with size 
wxw. 
2. Compute the mean value M(I) for each block using equation 1. 
           
                 1      w/2         w/2 
      M(I)  ―       ∑             ∑ I(i,j)                        (1) 
               W2    i=w/2      j=w/2 
3.use the mean value computed  in step 2 to compute  the 
standard deviation value std(I) from equation 2. 
 
 
 
 
ISSN: 2229 -7170
  
This work is licensed under a Creative Commons Attribution 3.0 License.  
 Pandey et al. Binary Journal of Data Mining & Networking 4 (1) 33-36 [2014] 
 
PAGE | 34 |
 
 
                     1      w/2       w/2 
      Std(I)=   ―     ∑           ∑    (I(I,j)―M(I))2      (2) 
                    W2      i=w/2   j=-w/2                           
 
4.Select Empirically a Threshold Value Working on Different 
Images. If the (1) is Greater than Threshold Value, the Block is 
Consider as foreground otherwise it Belongs to Background. 
Direction Based Methods  
This Method is Mainly Based on the Coherence of Direction. A 
fingerprint Consists of Parallel  line Structures the Coherence will 
be Considerable higher in the foreground Than in the 
Background[4]  
The  Steps for Direction Based Method [4][5] are Summarized as 
Follows; 
1. Dived the Input image I(i,j) into Non- Overlapping Blocks With 
Size wxw. 
2.Use 3x3 Sobel Vertical and Horizontal Marks Defined in 
Equalization 3 and 4 Respectively to Compute the Gradient dx (I,j) 
and dy(I,j) And at Each Pixel(I,j) which is the Center of the Block. 
 
                               -1    0    1 
Sobel Vertical =              -2    0     1      (3)                                         
                                       -1    0     1  
 
 
 
 
                              -1   -2    1  
Sobel Horizontal =            0    0    0  (4)                
                                        1    2    1  
 
3. Estimate the local Orientation using equation 5,6 and 7 [6] 
 
                                    i+w/2            j+w/2 
 Vx(i,j)  =    ∑                   ∑  (dx(u,v))(dy(u,v))      (5)      
                  U=i-w/2    v=j-w/2 
 
 
                    i+w/2            j+w/2 
 Vy(i,j) =   ∑                   ∑   dx2(u,v) - dy2(u,v)      (6)     
                   U=i-w/2        v=j-w/2 
 
                      i+w/2            j+w/2 
 Vz(i,j) =      ∑                   ∑ (dx(u,v) +dy(u,v))2     (7)     
                        U=i-w/2        v=j-w/2 
 
 
4. Calculate Background Certainly and Orientation field using 
equation 8. 
                   
      coh  =     (Vx2  (I,j) +Vy2  (I,j))           (8)   
                                w2*Vz 
 
5. select empirically a threshold value working on different images. 
If the coherence is greater than threshold value, the block is 
considered as foreground otherwise it belongs to background. 
In linear Hybrid Classifier for fingerprint Segmentation 
Method  
 A Hybrid algorithm based on linear classifiers for the segmentation 
of fingerprints is presented. The propose algorithm uses a Block-
wise classifier to separate foreground and background blocks in 
the main and employ a pixel wise classifier to deal with pixels 
accurately. In order to evaluate the performance of the new 
methods based on other classifiers. 
A captured fingerprint image usually consists of two components, 
which one is called the foreground and the background [2] .the 
foreground is the component that originated from the contact of a 
fingertip with the sensor. the noisy area at the borders of the image 
is called the background[3] 
Block-wise segmentation 
Block-wise segmentation includes foreground and background 
blocks to reduce computational consumption in subsequent 
procedure and avoid introducing false segmentation within small 
region. The fingerprint image portioned into blocks of 16*16 pixels. 
Two features block mean information and block variance 
information, are used in block-wise segmentation. For most 
fingerprint sensors, the ridge-valley structures can be 
approximated by black and white lines, while the background, 
where the finger does not touch the sensor is rather white. This 
means that the mean gray value in the foreground is in general 
lower, that is, darker gray, than it is in the background. 
 
                          1        15     15 
Mean(m,n)=   ―         ∑       ∑    I(i+16m,j+16n) 
                     16ï16    i=0    j=0 
 
In general, the variance of the ridge-valley structures in the 
foreground is higher than the variance of the noise in the 
background. The block variance for each block is given by:   
 
                                 1       15     15 
   Variance (m,n)=   ―       ∑      ∑{I(i+16m,j+16n) ă mean(m,n)}2 
                              16ï16   i=0   j=0 
 
 Each of blocks is assigned into three sets, foreground, background 
and pending blocks set, respectively. Using class w1 for the 
foreground block, class w0 for the background block, class w2 for 
the pending block, and w for the assigned class, the following 
decision function is applied; 
 
ω0   mean(m, n)< T1 , variance(m, n)<T2 
ω= ω1 mean(m, n)>T3, variance(m, n)>T4 
ω2 otherwise Where T1,T2,T3 and T4 are threshold values. 
 Pandey et al. Binary Journal of Data Mining & Networking 4 (1) 33-36 [2014] 
 
PAGE | 35 |
 
 
Pixel-wise segmentation 
Pixel-wise segmentation identifies and removes non-ridge pixels 
from the pending blocks. In this procedure, Coherence, Mean, and 
variance and selected as the pixel features. The coherence gives a 
measure how well the gradients are pointing in the same direction . 
In a window W around a pixel, the coherence is defined as 
 
                l ∑w(Gs,x,Gs,y)l          (Gxx-Gyy)2+4G2xy 
Coh=                                =  
                 ∑wl(Gs,x,Gs,y)l             Gxx+Gyy 
 
Where  (Gs,x,Gs,y) is the squared gradient, 
Gxx = ∑G2x      ,    Gyy =∑G2y       , Gxy =  ∑Gx Gy 
          W                     w                     w 
And (GX,GY) is the local gradient. 
           
The average gray value measures how gray the pixel is. Using   I  
as the intensity of the image, the local mean for each pixel is given 
by; 
 
Mean=∑ I 
                     W 
 
 The variance measures the gray variance around the local area. 
The variance is for each pixel is given by; 
 
Var =    ∑  (1-Mean )2  
                       W 
 
The Coherence, Mean and variance feature values are normalized 
in the [0, 1] range. The proposed pixel-wise segmentation is based 
on [8].  The algorithm presented in [8] uses a linear classifier which 
is called label box to enhance the performance of fingerprint image 
segmentation. The classifier uses three pixel features, being the 
coherence, the mean and the variance. The parameter modal is 
established using sample space at first. Then the type(background 
and foreground) of each pixel is pending blocks is decided by the 
parameters model and features. 
Low Quality Fingerprint Images 
This is a new algorithm to segment fingerprint Images [9]. These 
algorithm uses four features the global mean, the local mean, 
variance and coherence of the image to achieve the fingerprint 
segmentation. Based on these features a rule features a rule 
based system is built to segment the Image. 
The proposed algorithm is implemented in three stages; pre ă
processing, segmentation and post processing. Gaussian filter and 
histogram equalization are applied in the pre-processing stage. 
Segmentation is applied using the local features. Finally, fill the 
gaps algorithm and a modified version of threshold in the post-
processing stage. Segmentation stage. 
Feature for fingerprint segmentation     
Fingerprint features must reflect both the gray level of fingerprint 
and the direction of ridge lines . however the complicated 
construction of fingerprint  pattern  and the Imbalance  in the 
contrast, require local feature  Instead of the global features for 
gray-level based methods while coherence is the feature for 
direction based method . the combination for these features in one 
algorithm show efficiently the distribution of the pixels for ridges 
and valleys in the image. coherence feature indicates the strength 
of the local window gradients centered on the processed point 
along the same dominate  orientation. 
local mean, local variance and local coherence [7] are calculated 
as following  
 
                 1                               1 
mean =   ·   ∑ I   ,Variance =  ·  ∑  (I-mean)2  
               w2    w                      w2  w   
 
where I is Intensity and w is the window size Centered on the 
processed pixels. 
 
coh=           (gxx -gyy)2 +4(gxy)2 / (gxx+gyy) 
        
    gxx =∑Gx2 , gyy =∑ Gy2 ,gXy = ∑  Gx Gy 
                      w                 w                  w   
 
where Gx and  Gy are corresponding horizontal and vertical 
gradient  components which are given by sobel operators. 
   
   
 
Pre-Processing 
fingerprint image with low contrast, false traces ridges or noisy 
complex background cannot be segmented correctly. therefore it is 
required to enhance the image.  Gaussian filter used in this method 
and histogram equalization. Gaussian filter used to smooth the 
image and hence background areas. this step together with split 
and merge technique , with is applied in the next stage, will collect 
pixels with similar gray levels into big areas. histogram equalization 
is invoked in this stage too. when the global mean of the image 
under consideration is higher than a certain threshold, which mean 
a bright Image , histogram equalization is used to enhance the 
image by reduce the brightness of the image. 
Segmentation 
In this stage , Split and merge technique is applied to collect similar 
background areas after the smoothing process. in order to 
separate the foreground from the background, the image is divided 
into a number of non-overlapping sub ăimages of size 10X10 pixels 
and the local mean, local variance and local coherence are 
computed for each sub-image. the global mean together with the 
three a fore mentioned parameters are used to build a rule based 
Pre -
processing
Segmentatio
n
Post-
processing
 Pandey et al. Binary Journal of Data Mining & Networking 4 (1) 33-36 [2014] 
 
PAGE | 36 |
 
 
system to segment the foreground and background of the image 
under 
consideration. the result of this rule based system is to decide 
whether a certain block is a foreground or a background. 
Post Processing 
the segmentation fingerprint image may contain isolated 
background blocks which are surrounded by foreground blocks. 
obviously these background blocks are foreground in the original 
image . a simple post processing technique is proposed to 
eliminate the presence of these isolated blocks. 
Support Vector Machine (SVM) 
The key idea of SVM is generalization: A classifier need not only to 
work well on the training samples, but also       to work equally well 
on previously unseen samples. Although this is realized long 
before the appearance of SVM,  it is SVM that gives this idea a 
good implementation.  A linear SVM  is a separation  hyper plane 
with its separation margin maximized and the number of  mis-
classified samples minimized. The margin is defined as the 
distance between the hyper plane and the samples of the two 
classes that are closest to the hyper plane (among those being 
correctly classified). By minimizing the number of training errors, 
SVM seeks good performance on the training data; by maximizing 
the margin, the generalization ability for future data is optimized.we 
are using the new technique  for Fingerprint Image Classification is 
Support Vector Machine fingerprint classification is Important for 
different practical applications.  an accurate and consistent 
classification can greatly reduce fingerprint matching time for large 
database. 
 
 
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Survey of Different Segmentation Method for Low Quality Fingerprint Image

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