Journal of Signal and Information Processing, 20 11 , 2, 79 - 87
doi:10.4236/jsip.2011.22011 Published Online May 2011 (
Copyright © 2011 SciRes. JSIP
Online Fingerprint Verification Algorithm and
Distributed System
Ping Zhang1, Xi Guo1, Jyotirmay Gadedadikar2
1Department of Mathematics and Computer Science, Alcorn State University, Lorman, USA; 2Department of Advanced Technolo-
gies, Alcorn State University, Lorman, USA.
Email: {pzhang, jyo},
Received January 30th, 2011; revised March 5th, 2011, accepted March 7th, 2011.
In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning,
fingerprin t ac qu isition , image preprocessing, and feature extraction are conducted on workstations. Then, the extracted
feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based
database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the
type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae
features between the testing fingerprint and the references in the database. An elastically structural feature matching
algorithm is employed for featu re verificatio n. The proposed fingerp rint matching algorithm is insens itive to fing erprin t
image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on
poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing finger-
print and manipulate fingerprint feature database on the server through the internet. The proposed system performed
well on benchmark fingerprint dataset.
Keywords: Online Fingerprint Verification, Fingerprint Feature Extraction, Web Database Query
1. Introduction
Biometrics refers to the automatic identification of a
person based on his/her physiological or behavioral cha-
racteristics. Identification based on biometrics is pre-
ferred over traditional methods with the advantage that
biometrics identification techniques obviate the need to
remember a PIN/password which may be forgotten, or
the need to carry the tokens like passports and driver’s
licenses which may be forged, stolen, or lost. For exam-
ple, in the e-commerce application, the biggest concern is
security problem. Currently, the common security method
for online tran saction consists o f us ing on e’s p assword or
PIN. However, PIN is cumbersome and insecure as peo-
ple are afraid of passwords being stolen and the transac-
tion process being invaded by adept hackers. With the
increasing use of computers as vehicles of information, it
is necessary to restrict access to sensitive/personal data.
The biometric techniques can potentially prevent unau-
thorized access to or the fraudulent use of ATM, cellular
phones, smart cards, desktop PCs, workstations, and
computer networks. As a result, online biometric identi-
fication is enjoying a renewed interest.
Since manual fingerprint identification is extremely
tedious and time consuming, automatic and reliable fin-
gerprint identification systems are in great demand.
Many research achievements on fingerprint recognition
and identification have been published in literature re-
cently [1-4]. For example, fingerprint recognition and
verification techniques include point set matching [5,6],
graph matching [7], simulated annealing and genetic al-
gorithms [8], relaxation [9], neural network based me-
thod for classification [10], and structural method for
fingerprint recognition [11], etc.
To capture the texture information of fingerprint image,
Gabor filter with eight orientations is proposed by Jain et
al. [12]. Patil et al. [13] used four orientations of Gabor
filters for extracting fingerprint features from gray scale
fingerprint image. The image is cropped to the size of
128 × 128 pixels using its core point as the center. Huang
and Aviyente [14] and Khan and Javed [15] employed
wavelet based analysis in order to provide rich discrimi-
natory texture structure for fingerprint verification. To
capture global transformation between two fingerprint
images, genetic algorithm is adopted by Tan and Bhannu
Online Fingerprint Verification Algorithm and Distributed System
The combination of fingerprint minutiae and texture
information performs the better recognition rate than
individual methods. In the reference paper [17], the
global structure (orientation field) and the local features
(minutiae) are combined so that the global orientation
field is beneficial to the alignment of the fingerprints
which are either inco mplete or poor quality.
Ross et al. [18] proposed a new technique to estimate
the nonlinear distortion in fingerprint pairs based on
ridge curve correspondences. The nonlinear distortion,
called thin plate spline (TPS) function, is used to esti-
mate the “average” deformation model for a specific fin-
ger when several impressions of that finger are available.
Experimental results demonstrated that incorporating
their proposed model resulted in an improvement in the
matching performance.
Although texture-like feature can improve fingerprint
verification performance, minutiae pattern is the most
important feature. Generally speaking, minutiae extrac-
tion from distorted images is not reliable and often gives
erroneous results in matching.
In this paper, we will focus on online fingerprint veri-
fication over the internet. First, the basic concept of fin-
gerprint verification is reviewed in the introduction sec-
tion. Then, an online fingerprint verification distributed
system is drawn in Section II. Fingerprint acquisition and
image preprocessing are discussed in Section III. Both
minutiae feature extraction and dynamic matching algo-
rithm are proposed in Section IV while experiments are
conducted in Section V. A conclusion ends this paper.
2. Online Fingerprint Verification
Distributed System
Feature extraction plays an important role in the finger-
print verification system. Reliable and significant fea-
tures are extracted from fingerprint image. The local
features (ridge ending and ridge bifurcation) and global
features (core point) of fingerprint are defined as follows:
1) Ridge Ending: the point where a ridge ends abruptl y.
2) Ridge Bifurcation: the point where a ridge forks or
diverges into two bran ch ridges.
3) Core: The maximum curvature point. Some finger-
print has two cores.
4) Minutiae: ridge ending, bifurcation, and core are
called as the minutiae po ints.
The minutiae are shown in Figure 1. Advanced fea-
tures like loops, islands, and delta ( triangular portion) on
the fingerprint image can be formed by combining all of
the above minutiae points.
In the proposed system, live-scan fingerprint devices
are used as fingerprint acquisition. A live-scan finger-
print is directly obtained from the finger without the in-
termediate use of paper. For example, the Thompson-
CFS chip-based sensor works through thermal sensor to
detect temperature difference across the ridges and val-
leys. Siemens sensors are based on differential capaci-
The captured fingerprint image is a grayscale image
with 256 levels and different sizes (512 × 512, 256 × 256,
or 128 × 128) depending on application requirement. A
benchmark fingerprint database can be obtained from
National Institute of Standards and Technology (NIST),
USA. The NIST Special Fingerprint Database 4 [19] in-
cludes 2000 fingerprint image pairs, which was used to
test the proposed fingerprint algorithm.
In the proposed system, fingerprint image preprocess-
ing and feature extraction are conducted on computer
workstations. In order to effectively manage fingerprint
database on a server through the internet, the most ad-
vanced ActiveX Data Objects for .NET (ADO.NET) are
employed to conduct fingerprint database query. For
example, the operations of fingerprint enrollment, inser-
tion and deletion of a record, etc. are carried out on the
workstation sides and the changes in the database stored
on the server side. The fingerprint verification is proc-
essed over the internet in such a way that the workload of
server is decreased. Multiple workstations share one da-
tabase on the server. The fingerprint distributed system is
shown in Figure 2.
3. Fingerprint Image Preprocessing
Image filtering, image enhancement, and thinning are
indispensable to fingerprint image processing. In this
section, we will address these issues.
Figure 1. Fingerprint image with minutiae points.
Copyright © 2011 SciRes. JSIP
Online Fingerprint Verification Algorithm and Distributed System
Copyright © 2011 SciRes. JSIP
Figure 2. Online fingerprint verification distributed system.
3.1. Image Noise Removal and Enhancement 2) The pixels comprising the skeleton should lie in the
centre of the ridges;
Median filter [20] is used to remove salt-and-pepper
noises or spot-like noises. If a pixel is accidentally
changed to an extreme value caused by various reasons,
then the result of filter can achieve excellent result. The
advantage of median filter is to keep the edge of the im-
age and to remove salt-and-pepper noises in the images.
3) Skeleton pixel must be connected to each other to
form the same number of regions as existed in the origi-
nal image.
Figure 3 shows the processed result of one original
fingerprint image. Figure 3(a) is an original low-quality
fingerprint image scanned by an optical senor, Figure
3(b) shows the result image after median filter, histogram
equalization, thinning procedure.
Histogram Equalization [20] is applied to enhance the
ridges of the fingerprint images. Histogram Equalization
reassigns a new value of a pixel based on the image his-
togram. The algorithm works by dividing the image into
overlapping subimages with size of L × L; where L is the
length and width of the subimage. Histogram information
is obtained from local area of the fingerprint image.
3.3. Spur Detection and Refinement Procedure
After thinning, there exist either some spurs in the thin-
ning image or broken lines in the ridges. A ridge
smoothing algorithm is employed to delete the spurs and
to link the broken lines with following criteria:
3.2. Thinning Fingerprint Image If a branch in a ridge map is roughly orthogonal to
the local ridge direction and its leng th is less than a
specified threshold, then it will be removed.
Fingerprint images consist of ridges. The position and
direction of the ridges and the relationship among ridges
convey unique information. The extra pixels usually
comprise the thickness of the lines that need to be re-
moved in order to accurately extract minutiae points.
If a break line in a ridge is short enough and no
other ridges pass through it, then it will be con-
According to Zhang and Suen’s thinning algorithm [4],
a thinning process is much like erosion. The pixels to be
removed are marked in the first instance and then re-
moved in a second pass over the image. This process is
repeated until there are no more redundant pixels left.
The remaining pixels are those belonging to the skeleton
of the ridges and no minutiae po ints have been removed.
This is called thinning by successive deletion. The ske-
leton must remain intact and must have a few basic
properties listed below:
If several minutiae form a cluster in a small region,
then remove all of them except for the one nearest
to the cluster center.
If two minutiae are located close enough, facing
each other, but no ridge lie between them, then all
of them are removed.
4. Fingerprint Classification
4.1. Coarse Feature Classification
A fingerprint image can be classified as one of five cate-
1) It should consist of thin regions, one pixel wide;
Online Fingerprint Verification Algorithm and Distributed System
Figure 3. Original low-quality fingerprint image and the
preprocessed images. (a) Original fingerprint image1; (b)
Preprocessed image 1.
gories [2]:
Left loop
Right Loop
Tented Arch
A human expert can easily perform coarse finger clas-
sification. For an automatic system, the problem becomes
much more difficult due to poor image quality and im-
pression distortion. Two hierarchical classifications are
applied in our recognition system, namely, one coarse
classification and one fine classification.
The first step is to employ coarse classification method
[2] to classify fingerprint image into one of the five cat-
For the fine classification, further feature extraction
method needs to be investigated. Two most stable and
easily extracted features are ending points and bifurca-
tion, which are used as fine features in this paper.
4.2. Fine Feature Extraction
After image pre-processing and the coarse feature extrac-
tion, a thinned fingerprint image will be applied to fol-
lowing process:
Minutiae extraction: In order to extract minutiae in the
thinned fingerprint image, a 5 × 5 pattern template is
imposed on the image as shown in Figure 4.
In the minutiae extraction, the thinned image is
scanned twice to locate two kinds of points (ridge-ending
and bifurcation).
Assuming Point M is a detecting po int, the set of B(0 ),
B(1), , B(7) are its 3 × 3 neighboring points in a
clockwise direction beginning at top-left position; whereas
the set of A(0), A(1), A(2), , A(15) are its 5 × 5
neighboring points, which directions are shown in Fig-
ure 4.
If M is a ridge ending, the following criteria must be
Criterion 1:
 
 
1,8,8 2
1,16,16 2
CBk Bk
CAk Ak
 
 
if 0Bj
2,161, f1,3,5,7
Ajk ij
,8Bj presents the modulo operation of B(j)
with 8, where 8 is the total number of pixels in the 3 × 3
neighboring ring; whereas
,16Aj denotes the modulo
operation of A(j) with 16, where 16 is the total number of
the 5 × 5 neighboring ring.
The direction of the ending point M is assigned as one
of the directions A(0) ~ A(15), in which the direction of
the corresponden ce A(i) is non-zero. The position and the
direction are considered as ending point feature set.
For the bifurcation extraction, criterion 3 is applied.
A0 A1 A2 A3 A4
A15 B0 B1 B2 A5
A14 B7 M B3 A6
A13 B6 B5 B4 A7
A12 A11 A10 A9 A8
Figure 4. A minutiae extraction mask.
Copyright © 2011 SciRes. JSIP
Online Fingerprint Verification Algorithm and Distributed System83
 
 
1,16,16 6
CBk Bk
CAk Ak
 
We can modify the criterion 2 accordingly.
The direction of the bifurcation is assigned as one
ridge direction that has the maximum distance to the oth-
er two ridges. It can be expressed by Criterion 4 below:
If 1Aj
Then r1 and r2 are calculated based on the following
12 1
,16 0
Aj i
 
(1, 2, 3),015,lj
maxmax1 2 3
.max,,DIRDiffDD DD
where r1 is the number of the binary pixels, which values
are continuously equal to 0 on the 5 × 5 neighboring ring
counting in the clockwise direction, and starting from the
point A(j); whereas r2 has the same meaning in the anti-
clockwise direction.
l represents one of three ridges on the bifurcation area.
DIR is the direction of the bifurcation.
For example, in Figure 5, the central point is point M,
which is a bifurcation point with three ridges: Line
M-A14, Line M-A4, and Line M-A8. The number of 0’s
counted by Equation(4) in the its 5 × 5 neighboring pix-
els for Line M-A14 is 10; the number of 0’s for Line
M-A4 is 8; and the number of 0’s fo r Line M-A8 is 8. So
the direction of the bifurcation is the direction of Line
If there are two lines that have the same number of 0’s
in the 5 × 5 neighboring pixels, then first ridge line direc-
tion counted from top-left in the clockwise direction will
be considered as bifurcation direction.
A0 A1 A4
A14 M
A12 A8
Figure 5. Bifurcation point in the 5×5 neighboring area.
4.3. Fine Feature Match
An efficient and effective algorithm for feature matching
is the central theme of an automatic fingerprint identifi-
cation system. As its name implies, the matching process
deals with two fingerprint impressions captured at dif-
ferent times and in different environments. It will verify
whether or not the two fingerprints are identical. The
matching of two fingerprint impressions has proved to be
difficult. Three areas of concerns might be that: 1) the
minutiae of fingerprint impressions captured might have
different coordinates and relative angles; 2) the shape of
two fingerprint impressions taken at different places
might not be the same due to the effect of stretching; 3)
same fingerprint might have different impression images
as the different parts of fingerprint enrolled or as noises
introduced while enrolling.
An ideal automatic fingerprint system must satisfy the
following criteria: 1) the size of feature file must be
small in order to minimize search time and space; 2)
matching algorithm must be fast as well as accurate; 3)
matching algorithm must cope with the problems of rota-
tion, distortion, false minutiae, and omitting minutiae of
matching pairs; 4) matching algorithm must be robust if
two matching fingerprints only have partial images.
Based on the criteria mentioned above, a novel hybrid
matching approach is proposed in this paper. The algo-
rithm is divided two steps:
All minutiae are ordered according to the distance
from the fingerprint’s core.
The distance is represented by the number of ridges
between two feature points in the fingerprint im-
All the minutiae are listed according to the distance to
the core: {P0, P1, P2, , Pm-1}. Here m is the number of
Each feature set includes the following information:
Information of each minutia in the fingerprint image
must be recorded. The details of encoding information
are described as data structures below:
Struct Fingerprint Feature
unsigned char Type; // type of calculating minutia: end-
ing point (1) or bifurcation (2)
int DIR; // direction of the cal-
culating minutia
struct Neighborhood_Minutiea_Feature P[6];
POINTS Coordinates_xy; // coordinates of testing minu-
} Struct Neighborhood_Minutiea_Feature
unsigned char Type; // type of neighboring
Copyright © 2011 SciRes. JSIP
Online Fingerprint Verification Algorithm and Distributed System
minutiae: ending point (1) or bifurcation (2)
int LDIS;
// distance between the neighboring minutia and the cal-
culating minutia
int LDIR;
// direction difference between the neighboring minutia
and the calculating minutia
} For each minutia Pi, five neighboring minutiae which
are nearest to the minutia Pi are sorted. The five sorted
minutiae are represented by Pi1, Pi2, Pi3, Pi4, Pi5 respec-
The distance between the Pi and one of the neighbor-
hoods Pij is defined as:
LDIS[i,j] = the number of ridges between Pi and Pij; j =
1, 2,, 5.
The local direction difference between Pi and Pij is
calculated as follows:
LDIR[i,j] = DIR[i] – DIR[j]
where DIR[i] is the direction of calculating point Pi.
LDIR[i,j] is the directional difference between the calcu-
lating minutia Pi and its neighboring minutia Pij.LDIS[i,j]
and LDIR [i,j] are saved into the data structure: Neigh-
Based on our definition, the extracted features (dis-
tance and direction) are insensitive to fingerprint image
distortion and rotation.
Let the testing fingerprint
be represented as a set
of m minutiae features as follows:
FF F (5)
Note that each of the elements in the feature set is a
feature data structure containing the following compo-
11 1
22 233
44 45
,,.,. ,.,
,.,. ,.,. ,
., .,.,.,
.,., Coordinates_xy
ii iii
ii ii
Similarly, let the feature set of kth fingerprint image in
fingerprint database be represented as a set of n minutiae
points. Each minutiae point is represented by two data
structures defined in the Struct Fingerprint Feature and
Struct Neighborhood_Minutiea_Feature.
kkk kn
It is assumed that the two fingerprint images are
roughly aligned. The matching algorithm seeks to find
the number of matched minutiae between the testing fin-
gerprint and the reference fingerprint. The proposed
matching algorithm is elaborated as follows:
The distance between the ith minutia of testing finger-
print Fi and the jth minutia of the reference fingerprint Rkj
in the database can be calculated according to the dis-
tance integral norm:
*. .
iilkj jl
DijabsF TypeRType
The symbol “>” represents the data structure pointer
operation; parameters w1 and w2 are weight factors which
are empirically determined to give the maximum similar-
ity matching. In our experiments, w1 is set to 1.0 and w2
is set to 0.5.
Input: A set of structural features of the testing finger-
FF F and a set of structural features
in the kth reference fingerprint
kkk kn
Output: the ratio of the matched pair of two fingerprint
images to the average minutiae number of two finger-
print images (MF).
Count = 0;
Loop1: FOR (i = 1; i
m; ++i) {
Loop2: FOR (j = 1; j
n; ++j) {
If ((Type of Fi ==Type of Rkj). and. |Fi.DIR-Rkj.DIR|<
Compute D[i,j];
D[i]min = min(D(i,j – 1), D(i,j));
// Write down the value j of reference fingerprint, which
has minimum distance with minutiae i in testing finger-
} // end of Loop 2
If (D[i]min <
Count = Count + 1;
// increment the matched minutiae numbers; A paired
testing minutia will not be paired again;
} // end of Loop 1
MF = 2*Count/(m*n);
are two parameters depending on the
image resolutions and application conditions. In our ex-
is set to 0.75 and
is set to 5.0. MF is a
parameter to judge the matching degree. The higher the
MF is set, the higher possibility the two fingerprints will
get matched.
4.4. Discussion of Proposed Algorithm
As the distance between two feature points on a finger-
Copyright © 2011 SciRes. JSIP
Online Fingerprint Verification Algorithm and Distributed System85
print image is measured by the number of ridge/valley,
the distance feature is therefore insensitive to scale and
distortion. In addition, direction feature is the difference
between the direction of testing minutia and that of its
neighboring minutia, which is image-rotation insensitive.
As a result, the proposed fingerprint feature extraction
method is insensitive to image distortion and rotation.
Another advantage is that an elastically structural fea-
ture matching method proves to be a fast and reliable
solution for fingerprint veri fication and iden tification.
5. Experiments
NIST Special Fingerprint Database IV [19] is used in the
experiment. The database contains 2000 fingerprint im-
age pairs with 8-bit grayscale images. Some poor quality
fingerprints images are severely deformed due to rolling
and/or inking problems of the distorted ridges.
In the first experiment, 200 fingerprints from NIST
Special Database 4 Fingerprint Database were chosen to
test the proposed system. By using PC computer with 4
MB of internal memory and 2.50 GHz, The system
shows that the image preprocessing which includes im-
age filtering, enhancement, and thinning for a 256 × 256
grayscale fingerprint image, needs about 150 ms. The
minutia feature extraction and encoding takes 100 ms. It
takes about 200 ms to match a testing fingerprint with
200 enrolled fingerprints in the database. As shown in
Figure 2, image preprocessing and feature extraction
were conducted on individual workstation computers.
The fingerprint match (verification) is through the inter-
net and conducted on a computer server, where enrolled
fingerprint features were saved into database. ActiveX
Data Objects for .NET was used for web-based database
query. Clients can use ADO.NET on their workstation
computer to remotely control the fingerprint feature da-
tabase on the server, such as fingerprint feature enroll-
ment, deletion, fast sorting, query, etc.
Figure 6 gives two examples of original fingerprint
images and image preprocessed (noise removal, image
enhancement and thinning) images. In the Figures 6(c)
and 6(d), the blue points highlight the ending points and
the bifurcation points are marked in red. In each thinned
image, one feature point and its five nearest neighboring
points are linked for visualizing feature extraction data
As for the deteriorated fingerprint image pairs in the
data set, it is very difficult to match all the five neighbor-
ing feature points using Equation (8) due to missing mi-
nutiae and false minutiae occurring in the processed im-
age. A flexible scheme is proposed in the applications.
The different numbers of neighboring feature points are
chosen to conduct fingerprint verification in order to
achieve the highest recognition rate. Table 1 shows the
relationship of the False Acceptance Rate (FAR), False
Figure 6. Original fingerprint images and feature extracted
images. (a) original fingerprint image1; (b) original finger-
print image 2; (c) feature extracted image 1; (d) feature
extracted image 2.
Copyright © 2011 SciRes. JSIP
Online Fingerprint Verification Algorithm and Distributed System
Table 1. FAR and FRR with different number of neighbor-
ing feature points in the fingerprint matching.
No. of Neighboring
Feature Points False Acceptance
Rate (FAR) False Reject Rate
1 20.00% 0.50%
2 15.50% 1.00%
3 4.50% 2.50%
4 1.50% 5.00%
5 0.5% 6.00%
Figure 7. Fingerprint verification rates on different enroll-
ment number of images.
Reject Rate (FRR) with the different number of neighbor-
ing feature points in the fingerprint image matching.
In the second experiment, a comparative experiment
on the different number of fingerprints was conducted.
The recognition performance with different enrollment
numbers of fingerprint is shown in Figure 7.
For 200 fingerprint images, the verification rate (cor-
rect passing rate) can be as high as 95%.
6. Conclusions
Fingerprint verification over the internet is a new re-
search topic as it will deal with web-based database
query and fingerprint feature transmission over internet.
A web-based fingerprint verification algorithm and its
distributed system is presented in this paper. Based on
internet transmission, no extra hardware system is
needed. A new fingerprint feature extraction method and
a flexible structural feature matching algorithm are pro-
posed in the system. Experiments show that the proposed
fingerprint verification algorithm is insensitive to finger-
print image distortion, scale, and rotation. The web-based
online distributed system performs well on the bench-
mark fingerprint dataset and practical applications.
Further research will be conducted on how the net-
work security can be applied to fingerprint feature trans-
mission over the internet. The feature data should be en-
crypted before transmission in order to protect the system
from being invaded by adept hackers.
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