The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
The Concrete Scheme of Pattern Matching
In Reconstruction Fingerprint For
Improving Accuracy
N.Kannaiya Raja1, K.Arulanandam2, and R. Somasundaram3
1.A.P/CSE Dept. Arulmigu Meenakshi Amman College of Engg, Thiruvannamalai Dt, near Kanchipuram Email:[email protected]
2. Prof & Head, CSE Department Ganadipathy Tulsi‘s Jain Engineering College,Vellore
3. Arulmigu Meenakshi Amman College of Engg,Thiruvannamalai Dt, near Kanchipuram.
E-mail: [email protected]
Abstract— Fingerprint system use in the pixel system for interacting to the problem of many
fields. In this fingerprint system has generally represented by four schemes: grayscale image,
phase image, skeleton image, and minutiae scheme which are used in this paper to find out
spurious minutiae in the fingerprint. Most of the fingerprint reconstruction schemes has been
existed which based on converting minutiae representation to phase (continuous phase and spiral
phase).but this still contain a few spurious minutiae especially in high curvature region. For a
direct use of the existing reconstruction algorithm to a latent fingerprint in NIST SD27. Both the
ridge flow and minutiae in the reconstructed fingerprint match the original fingerprint well. But,
apparently, the reconstructed ridge pattern does not match the original ridge skeleton exactly.
This novel reconstruction method proposed the difficult and important problem of latent
fingerprint restoration using significantly modified existing reconstruction algorithm to make the
reconstructed fingerprints appear visually more realistic, brightness, ridge thickness, pores, and
noise should be modeled. The accept rate of the reconstructed fingerprints can be further enhance
by reducing the image quality around the spurious minutiae in the grayscale image and other
features (such as ridge orientation and skeleton) manually marked by the latent expert.
Keywords— reconstruction, enhancement, minutiae, ridge matching, curve matching
I. INTRODUCTION
Fingerprints have been used in identification of individuals for many years because of the
famous fact that each finger has a unique pattern. Many fingerprint identification and verification
methods have been proposed, such as image correlation [1], graph matching [2], structural
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matching [3,4], and matching with transform features [5], and so on. Among them the most
widely used one is methods based on point pattern matching [6,7]. However, fingerprint
recognition is still a challenging problem due to the following difficulties: (1) Low quality
fingerprint images are often captured due to dry or wet skin, dirty or injured fingers, and non
uniform pressures. Feature detector operated on such images will miss genuine minutiae and
introduce spurious minutiae. (2) The solid-state sensors are increasingly used, which capture
only a portion of a finger. This causes the deficiency of minutiae information. In such a case, it is
difficult to make a reliable decision whether two fingerprints are from the same finger. (3) The
imaging process introduces elastic deformation in ridge pattern and minutiae locations of the
fingerprint image. While large bounding box can be used during matching to tolerate it, the sideeffect is that the false accept rate will increase. In recent years, new representations of fingerprint
image and new matching algorithms have been proposed to resolve the problems above. Finger
Code representation and matching scheme introduced by Jain et al. [5], which captures the global
and local features of fingerprints, is robust to low quality images and has an advantage of fixedlength feature vectors. Ross et al. [8] present a hybrid matcher that combines minutiae and
texture features. Tico and uosmanen [9] introduce an orientation-based minutia descriptor to
identify corresponding minutiae and compute the matching score. Kovács-Vajna [10] uses a
triangular matching method to deal with the nonlinear deformation, which is based on the fact
that local distortion is less than global distortion.
Fingerprint matching is the task of comparing a test fingerprint that is actually provided,
to a template fingerprint that is provided earlier during enrollment. Most fingerprint matching
systems are based on the minutiae, which are the endpoints and bifurcations of the line structures
in the fingerprint that are called ridges. A minutiae-based fingerprint matching system roughly
consists of two stages. In the minutiae extraction stage, the minutiae are extracted from the grayscale fingerprint, while in the minutiae matching stages, two sets of minutiae are compared in
order to decide whether the fingerprints match. This paper deals with the compensation of elastic
distortions for the sake of improving the performance of minutiae matching.
In minutiae matching, two stages can be distinguished. First, registration aligns both
fingerprints as well as possible. Most algorithms use a combination of translation, rotation and
scaling for this task. After registration, the matching score is determined by counting the
corresponding minutiae pairs between both fingerprints. Two minutiae correspond if a minutia
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
from the test set is located within a bounding box or tolerance zone around a minutia from the
template set. The matching score, which is a number in the range from 0 to 1, is calculated as the
ratio of the number of matched minutiae to the total number of minutiae. Unfortunately, there are
a lot of complicating factors in minutiae matching. First of all, both sets may suffer from false,
missed and displaced minutiae, caused by imperfections in the minutiae extraction stage. Second,
the two fingerprints to be compared may originate from a different part of the same finger, which
means that both sets overlap only partially. Third, the two prints may be translated, rotated and
scaled with respect to each other. The fourth problem is the presence of non-linear plastic
distortions or elastic deformations in the fingerprints, which is the most difficult problem to
solve.
Bazen [11] estimates nonlinear distortion between two minutiae sets using thin-plate
spines, and removes the distortion prior to the matching stage. All these methods perform
reasonably well in certain circumstances; some methods do well for low quality fingerprints,
some do well when nonlinear deformation exists, and others do well when the overlapped region
of two fingerprint images is small. In this paper, however, a novel approach based on ridges is
proposed with the aim to try to solve all these problems. Ridge image, also called thinned image
or skeleton image, is an intermediate image in many feature extraction algorithms. Since
minutiae are generally thought of as enough to identify a person, ridge image is just used to
extract minutiae from it. After that, the ridge image is discarded.
(a)
(b)
(c)
Figure 1. (a) Intensity image, (b) ridge image, (c) synthesized image.
However, we think ridge images have much more usages. In our opinion, ridge image has
the following features:
(1) Ridge image is an effective representation of the fingerprint image. From a ridge image, we
can synthesize an image similar to the enhanced version of the original fingerprint image.
On the contrary, it is definitely impossible to do so from a minutia set. An example of a ridge
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image and its synthesized image is given in (Fig.1). The synthesized image is generated using the
following steps. to compute the distance transform of a ridge image (b), to replace the intensity
value greater than a threshold T (e.g. 5) with T, and to scale the intensity range [0 T ] to [0 255],
then we can obtain a synthesized image (c) that looks like the enhanced version of the original
fingerprint image (a).
(2) Ridge image is also a compact representation of the fingerprint image. Ridge images
can be efficiently approximated by polygonal lines, so the size of a template file is small. We
have conducted an experiment and found that for fingerprints in FVC2002 DB1 (388*374, 500
dpi), the average size of template files is 1.3 Kbytes, which meets the storage requirement of
"light" system in FVC2004 competition [12].
(3) Similar minutiae patterns do not mean similar ridge patterns. Actually from
experiments, we observed that the ridge patterns of most different fingerprints which have similar
minutiae patterns are significantly different.
(4) Unlike minutiae whose distribution on a fingerprint seem to be random, ridges cover
the whole region of a fingerprint. As a result, with the reduction of the effective region of two
fingerprints, the performance of the ridge-based system will not degrade dramatically.
(5) The topology information in ridge patterns is reliable (especially in the direction
normal to ridges) and invariant to nonlinear distortion. Ridges have been used for different
purposes by some researchers. Ridges associated with corresponding minutiae are used to align
two minutiae patterns by Jain [7].
Fingerprint classification algorithm in [13] is based on features extracted from ridge
images. Ridges associated with corresponding minutiae are used to estimate the nonlinear
distortion between two fingerprints [14]. Although ridges have been used in a number of aspects
related to fingerprint recognition technology, to our knowledge, there have been no work
published that reports matching two ridge images directly. The algorithm proposed in this paper
is novel on that it establishes both the ridge correspondences and the minutia correspondences
between two fingerprints. The algorithm consists of three stages: preprocessing, alignment, and
matching.
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
Figure 2. (a) Intensity image (500 dpi), (b) ridge image before clear-up, (c) ridge image after
clear-up and sampling (the sampling interval is 6 pixels, and the sampled points are connected).
In the preprocessing stage, ridges are extracted from the thinned image and sampled
equidistantly, and relations between ridges and minutiae are established. In the alignment stage, a
set of N initial substructure pairs is found using a novel approach. In the matching stage, for each
of the N initial substructure pairs, ridge matching is performed to produce a matching score.
Finally, the maximum of the N scores is used as the final matching score of the two fingerprints.
The idea underlying our alignment algorithm focuses on how to choose a reliable local feature
pair as the base of matching. This is accomplished first by defining a substructure that contains
as much local information (one minutia and several ridges) as possible, and secondly by finding
the substructure pair which have the most consistent substructure pairs around. In our matching
algorithm, during the process of ridge matching, minutiae are also paired, and the matching score
is computed according to both the matched minutiae and the matched ridges. Many existing
algorithms use a single global transformation to align two fingerprints [7, 9]. Different from
these algorithms, we apply different local transformations in different regions. The
transformation is estimated using matched substructures, and applied to nearby ridges.
Experiments have been conducted on FVC2002 databases [15] and the preliminary results have
demonstrated the validity of the proposed approach.
The rest of the paper is organized as follows. The next three sections are, respectively,
devoted to the three stages of the algorithm, namely, preprocessing, alignment and matching. In
Section 5, the experimental results and evaluation are presented. Finally in Section 6, summary
and plans for future work are given.
II. RELATED WORKS
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FINGERPRINT recognition systems play a crucial role in many situations where a
person needs to be verified or identified with high confidence. As a result of the interaction of
genetic factors and embryonic conditions, the friction ridge pattern on fingertips is unique to
each finger. Fingerprint features are generally categorized into three levels (Fig. 3):
1. Level 1features mainly refer to ridge orientation field and features derived from
it, i.e., singular points and pattern type.
2. Level 2 features refer to ridge skeleton and features derived from it, i.e., ridge
bifurcations and endings.
3. Level 3 features include ridge contours, position, and shape of sweat pores and incipient
ridges.
Due to its distinctiveness, compactness, and compatibility with features used by human
fingerprint experts, minutiae-based representation has become the most widely adopted
fingerprint representation scheme. But other representation schemes do show strong
performance, i.e., Bioscrypt‘s algorithm in FVC2002 and FVC2004 (Fingerprint Verification
Competition) [8].
Some minutiae-based matching systems also employ additional features, i.e., orientation
field, singular points, ridge count, etc., to improve the matching accuracy. In these representation
schemes, the grayscale image has the most information and features at all three levels are
recorded (depending on the sensor); compared to grayscale image, phase image and skeleton
image lose all Level 3 features and compared with phase image and skeleton image, the minutiae
template further loses some Level 2 Information, such as ridge path between minutiae.
The widespread deployment of fingerprint recognition systems in various applications
has caused concerns that compromised fingerprint templates may be used to make fake fingers,
which could then be used to deceive all fingerprint systems the same person is enrolled in. Once
compromised, the grayscale image is the most at risk. Leakage of a phase image or skeleton
image is also dangerous since it is a trivial problem to reconstruct a grayscale fingerprint image
from the phase image or the skeleton image.
Fig.5. shows the reconstructed grayscale image from the phase image ¥(x, y) by cos (T(x,
y)) and that from the skeleton image by distance transform. In contrast to the above three
representations, leakage of minutiae templates has been considered to be less serious as it is not
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
trivial to reconstruct a grayscale image from the minutiae.
However, several researchers [16], [17], [18] have shown that it is possible to reconstruct
a fingerprint image from the given minutiae template. The methods of Hill [16] and Ross et al.
[17] first reconstruct a skeleton image from minutiae, which is then converted into the grayscale
image. in [16], the orientation field is generated based on singular points according to the model
in [22]. A line drawing algorithm is used to generate a sequence of splines passing through the
minutiae. In [13], the orientation field is estimated using selected minutiae triplets in the
template. Streamlines are then traced starting from minutiae and border points. Linear Integral
Convolution is used to impart texture-like appearance to the ridges. Finally, the image is
smoothed to obtain wider ridges. This reconstruction algorithm can only generate a partial
fingerprint. In addition, streamlines that terminates due to pattern. A rendering step is performed
to make the reconstructed fingerprint image appear more realistic. The efficacy of this
reconstruction algorithm was assessed by attacking nine fingerprint matching algorithms. An
average True Accept Rate (TAR) of 81.49 percent at 0 percent False Accept Rate (FAR) was
obtained in matching 120 reconstructed fingerprints against the 120 original fingerprints in
FVC2002 DB1.
However, this algorithm also generates many spurious minutiae in the reconstructed
fingerprints. Fingerprint reconstruction from minutiae (hereinafter simply referred to as
fingerprint reconstruction) is very
(a)
(b)
(c)
(d)
Figure 3. Features at three levels in a fingerprint. (a) Grayscale image (NIST
SD30, A067_11), (b)Level 1 feature (Orientation field), (c) Level 2 feature (ridge
skeleton), and (d) Level 3 features (ridge contour, pore, and dot).
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Distance constraint between adjacent streamlines will generate spurious minutiae. The validity of
this reconstruction algorithm was tested by matching 2,000 reconstructed fingerprints against the
2,000 original fingerprints in NIST SD4. A rank-1 identification rate of 23 percent was reported.
Cappelli et al. [18] proposed a technique to directly reconstruct the grayscale image from
minutiae. The orientation field is estimated by fitting a modified model initially proposed in to
the minutiae directions.
Similar to fingerprint synthesis [21] except that the goals and the inputs of the two
techniques are different. The goal of fingerprint reconstruction is to obtain an artificial
fingerprint that resembles the original fingerprint as much as possible, while the goal of
fingerprint synthesis is to generate any artificial fingerprint that is as realistic as Possible. For
fingerprint reconstruction, the minutiae from a given fingerprint must be provided, while for
fingerprint synthesis, images).
(a)
(b)
(c)
(d)
Figure 4. Fingerprint representation schemes. (a) Grayscale image (FVC2002 DB1, 191), (b)
phase image, (c) skeleton image, and (d) minutiae.
no input is needed (except for a statistical model of fingerprint learned from many real
fingerprint The well-known SFINGE fingerprint synthesis method of Cappelli et al. [21]
performs Gabor filtering on a seed image according to the orientation and frequency images;
minutiae automatically emerge during the filtering procedure. Some interclass variations, such as
spatial transformation, touching area, nonlinear distortion, ridge dilation/shrinking, and noise, are
simulated to generate realistic impressions of the master fingerprint. One main limitation of
SFINGE is that minutiae cannot be controlled. As a result, SFINGE may generate problematic
fingerprints that contain too few minutiae or very long ridges. It is well known that the
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
distribution of minutiae in fingerprints is not random and fingerprints of different pattern types
have different Minutiae distributions [17]. The minutiae distribution of fingerprints generated by
SFINGE may not conform to such distributions since these minutiae are automatically generated
during the image filtering process. Similar fingerprint synthesis methods have also been
proposed in [19], [20]. The reaction-diffusion technique described in can also be used for
synthesizing fingerprints.
Figure 5. Reconstruction of grayscale fingerprint image (FVC2002 DB1,19_1, see Fig. 2a).
(a) Reconstructed from phase image and (b) Reconstructed from skeleton image.
Bicz described a fingerprint synthesis technique based on the 2D FM model. The phase of the
FM model consists of the continuous Component and the spiral component, which corresponds
to minutiae. The advantages of our approach over existing approaches to fingerprint
reconstruction [16], [17], [18] are: 1) A complete fingerprint can be reconstructed and 2) the
reconstructed fingerprint contains very few spurious minutiae. The proposed reconstruction
algorithm has been quantitatively assessed by matching reconstructed fingerprints against the
corresponding original fingerprints (termed
as
type-I
attack)
and
against
different
impressions of the original fingerprints (termed as type-II attack) using a commercial fingerprint
SDK, Neurotechnology VeriFinger 4.2. Type-I attack was found to have a high chance of
deceiving the fingerprint recognition system in both the verification and identification
experiments. Type-II attack also has a significantly higher accept rate than that of impostor
match. A TAR of 94.13 percent at a FAR of 0 percent has been observed in the verification
experiment conducted on FVC2002 DB1, and 99.70 percent rank-1 identification rate has been
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observed in the identification experiment conducted on the NIST SD4 database.
III. MINUTIAE BASED ORIENTATION FIELD RECONSTRUCTION
In this section, we describe the method proposed in [23] to reconstruct the orientation
field based on minutiae and singular points, if available. Here we assume that a latent expert has
marked the minutiae and singular points. Also, in cases in which an odd number of singular
points were marked in the region of interest, paired singular points outside of the region of
interest are guessed (referred to as virtual singular points [24]) to satisfy the constraints of
singularity number, which states the total number of singular points is even, and the numbers of
cores and deltas are the same. Because of the reasons mentioned in Section II, the orientation
field is also computed in non-overlapping blocks of a predefined size (e.g. 8x8 or 16x16). Now,
consider lines passing through the non-overlapping blocks that divide the image into 8 equally
spaced sectors then, a local orientation field estimate are obtained for each block based on the
direction of the nearest minutia in each of the 8 sectors. Let {xn, yn, an}, 1 < n < N, be a set of N
fingerprint minutiae marked by a latent expert, where (xn, yn) is the location and an is the
direction of the
th
minutia. Then, by doubling the minutia direction ak, which means taking 2 a*
instead of ak as the minutia direction, it becomes equivalent to ak + ^-(this is necessary since
fingerprint ridges are not oriented). For the K minutiae selected in eight sectors, cosine and sine
components can be computed and summed as follows:
K
u = y cos(2ak) wk,
(1)
fc-i
K
v = y sin(2ak) wk,
(2)
fe-i
here wk is a weighting function based on the Euclidean distance between the block center and the
fcth minutia that makes the closest minutia direction dominates the ridge orientation of
neighboring blocks.
The orientation at block (m, n) is then computed as:
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
We also consider that singular points have been marked in some of the latent fingerprints. In
those cases, the direction field of Ns singular points is given by the Zero-Pole model
where msi , nsi , and si (core: 1, delta: -1) are the location and type of the ith singular point.
Orientation field is first estimated using minutiae whose direction is subtracted by Ds. The
reconstructed orientation field is then given by
If there are no marked singular points, the orientation field is reconstructed simply as D(m, n).
orientation field estimated directly from the gray scale image, the third column shows the
reconstructed orientation field and the fourth column shows manually marked orientation field.
We can observe that it is not easy to estimate the orientation field from the latent image. But, the
reconstructed orientation field from minutiae and singular points is quite reliable, although it is
not as smooth as manually marked orientation field.
IV. FINGERPRINT IMAGE ENHANCEMENT
Automatic and reliable extraction of minutiae from poor quality images is a very difficult
problem. Image enhancement is one way of improving the matching performance, as shown in
[12], where a method based on the estimated local ridge orientation and frequency is proposed to
improve the clarity of ridges and valleys.
For latent images, it is very difficult to obtain a reliable orientation field based on the
image itself. Also, the ridge frequency estimated from the latent image is not reliable. Therefore,
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we applied the enhancement process proposed in [23] using the reconstructed orientation field
described in Section II-A and the median ridge frequency estimated in small image blocks. Fig.8
shows some examples of enhanced latent images, along with their skeletons, the corresponding
enhanced images and their
(e)
(f)
(g)
(h)
Figure 6. Comparison of orientation field estimation methods: (a) and (e)
Original latent Fingerprint images, (b) and (f) orientation fields estimated
directly from the given images, (c) and (g) orientation fields estimated from
minutiae and singular points and (d) and (h)
Fig.6. shows some examples of reconstructed orientation field. The first column shows the
original latent image, the second column shows the enhanced image skeletons. It can be noted
from the figure that the clarity of the ridges improves and the noise is greatly reduced.
ASSUMPTIONS
In the paper, two terms, ridge ends and bifurcations, are used. These are conventional
terms used in fingerprint literature to refer to minutiae. In this work, however, they are not taken
in meaning as feature points. Though similar in definition, here the two features are two points in
ridges having certain properties (ridge-ends are where a ridge breaks and bifurcation in point
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
where a ridge splits) and are used to reconstruct a low quality skeleton image. The conventional
terms are used merely for better understanding of the reader.
The reconstruction steps have been designed with the assumption that no previous global
enhancement has been done. Thus the algorithm is formed keeping in view the poor quality
image cases. An initial enhancement when applied would not in any way contribute to degrading
the used to crack the existing fingerprint recognition system [1, 2], but can also be used to
improve the fingerprint matching accuracy [23]. The orientation field plays a critical role in the
whole reconstruction procedures. Ross et al. [24] proposed to estimate the orientation field by
selecting well structured triangles from the minutia set and computing the orientation values
within each triangle by interpolation. This method fails to estimate the orientations
Where minutiae are not enough. To address this problem, Chen et al. [23] proposed to
add some virtual minutiae and use Delaunay triangulation. However, both the Ross and Chen
methods can only estimate the orientations inside the convex hull of minutiae.
Feng and Jain [23] proposed a directional minutiae weighted method that can estimate
orientations outside the convex hull of minutiae. In Feng‘s method, prior information about ridge
flow is not considered. In this Letter, we propose a Gaussian
(e)
(f)
(g)
(h)
Figure 7. Examples of enhanced latent images. (a) and (e) are original latent
images, (b) and (f) are the skeletons of the original latent images extracted by
VeriFinger, (c) and (g) are enhanced images using reconstructed orientation
field, and (d) and (h) are the skeletons of the enhanced
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images extracted by VeriFinger
Secondly, for the development of the algorithm it was assumed that the fingerprint area
was already segmented. In all the binary skeleton images '0‘ represents the ridge and '1‘ is the
background.
ANALYSIS
The minutia set is the most widely used fingerprint feature. Recent progress [24] in
fingerprint reconstruction has shown that, from the minutia set only, we can obtain much
information about a fingerprint. These features can not only be weighted orientation field
reconstruction model from the minutia set, which can simulate the ridge flow characteristic more
accurately. Our method is inspired by the observation that variation along the parallel ridge
direction. We assign different weights to these directions. To address the problem that minutiae
cluster in a local region, we use a minutia density indicator to decrease the weight accumulated
from the dense minutiae region. After Gaussian weighted reconstruction, the FOMFE model is
used to further smooth the orientation field. Orientation reconstruction: Suppose that the input
minutia template is represented by M = {(xi, yi,ui)}Ni =1, where N is the number of minutiae in
the template. The orientation field f (x, y) can be estimated as follows:
Estimated from
the
minutia
set independently and finally combined to get the
orientation field f from
Where tan21 is a four quadrants arctangent function.
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
And ni is the number of minutiae that locate within a circle of radius recentred at (xi, yi) (r is 30
pixels in this Letter). dx and dy represent the range of the minutiae that affects along the parallel
direction and perpendicular direction, respectively. We find by experience that minutiae have
different degrees of affection along their parallel and perpendicular directions. Usually, the
orientations along the parallel direction have larger variations and the orientations along the
perpendicular direction have smaller variations.
V. EXPERIMENTAL RESULTS
Matching experiments were conducted on the NIST Special Database 27, which consists
of 258 latent fingerprint images and 258 mated rolled fingerprint images. NIST SD27 contains
images of three different qualities, termed "good", "bad", and "ugly". Some examples of these
images were shown in Fig. 2. The manually marked features in the latents in this database are
region of interest (ROI), minutiae, visible singular points (inside the ROI) and "virtual" singular
points (outside the ROI). To make the matching problem more challenging and realistic, the
background database (gallery) was increased from 258 mated rolled fingerprints to 27, 258 total
Step 1: The orientation field f (x, y) to be reconstructed can be represented as fc(x, y) =
cos(2f (x, y)) and fs(x, y) = sin(2f (x, y) to address the singularity problem of orientation. Fc and fs
will be rolled fingerprints by adding 27,000 fingerprint images from the database NIST SD14.
For the rolled fingerprint images, only minutiae were needed for matching and they were
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automatically extracted using Verifinger [17].
For boosted max, the transformations were computed based on the matched minutiae
output by Verifinger for each pair of fingerprints being matched.
VI. CONCLUSION AND FUTURE WORK
We propose a robust orientation field reconstruction algorithm from the minutia set by
considering the ridge flow characteristics of a fingerprint. Latent matching is a very difficult
problem due to their poor quality and small area. A fully automatic system is desired, but given
the difficulty of the problem and the poor performance of available AFIS, manual input is still
needed. we have shown that the performance of manually marked Minutiae in latents can be
improved by utilizing automatically extracted minutiae from enhanced latent images. This
framework improved the latent matching performance irrespective of their quality.
To make the matching problem more challenging, realistic and the conclusions more
reliable, the backgrounds database (gallery) was increased from 258 mated rolled fingerprints to
27, 258 totals rolled fingerprints by adding 27,000 fingerprint images from the database NIST
SD14. Although the reconstructed orientation field is comparable to ground truth orientation
field, it is not completely accurate. During image enhancement, if the estimated orientation field
in a block is not reliable, spurious minutiae can be created. Therefore, improving the orientation
field reconstruction is necessary for better performance. We used manually marked ROI to
reconstruct the orientation field and enhance the image only inside that region. Our ongoing
work reconstructs a larger part of the latent image to use in the matching process.
Figure 8. CMC curves for manually arked minutiae, enhanced image using econstructed
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orientation field, automatically extracted minutiae from latent image and enhanced image using
manually marked orientation field.
Figure 9. CMC curves for manually marked minutiae, enhanced image using
reconstructed orientation field, highest score rank-level fusion and Borda count fusion
REFERENCES
[1] C.Wilson, C.Watson, E. Paek, ―Effect of resolution and image quality on combined optical
and neural network fingerprint matching‖, Pattern Recognition 33 (2) (2000) 317–331.
[2] D.K. Isenor, S.G. Zaky, ―Fingerprint identification using graph matching‖, Pattern
Recognition 19 (2) (1986) 113–122.
[3] A.K. Hrechak, J.A. McHugh, ―Automatic fingerprint recognition using structural
matching‖, Pattern Recognition 23 (8) (1990) 893–904.
[4] A. Wahab, S.H. Chin, E.C. Tan, ―Novel approach to automated fingerprint recognition‖, in:
Proceedings of IEE Visual Image Signal Processing, 1998, pp. 160–166.
[5] A.K. Jain, S. Prabhakar, L. Hong, S. Pankanti,‖ Filterbank-based fingerprint matching‖,
IEEE Trans. Image Process. 9 (5) (2000) 846–859.
[6] N.K. Ratha, K. Karu, S. Chen, A.K. Jain, ―A real-time matching system for large fingerprint
databases‖, IEEE Trans. Pattern Anal. Mach. Intell. 18 (8) (1996) 799–813.
[7] A.K. Jain, L. Hong, R. Bolle, ―On-line fingerprint verification‖, IEEE Trans. Pattern Anal.
Mach. Intell. 19 (4) (1997) 302–313.
[8] A. Ross, A.K. Jain, J. Reisman, ―A hybrid fingerprint matcher‖, Pattern Recognition 36 (7)
| ACS-International Journal in Computational Intelligence, Vol-3,Issue-1 Mar 2012 | 75
The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
(2003) 1661–1673.
[9] M. Tico, P. Kuosmanen, ―Fingerprint matching using an orientationbased minutia
descriptor‖, IEEE Trans. Pattern Anal. Mach. Intell. 25(8) (2003) 1009–1014.
[10] Z.M. KovQacs-Vajna, ―A fingerprint verification system based on triangular matching and
dynamic time warping‖, IEEE Trans. Pattern Anal. Mach. Intell. 22
(11) (2000) 1266–1276. [11] A.M. Bazen, S. Gerez, ―Fingerprint matching by thin-plate spline
modeling of elastic deformations‖, Pattern Recognition 36 (8) (2003)1859–1867.
[12] The 3rd Fingerprint Verification Competition, http://bias.csr.unibo.it/fvc2004/.
[13] A. Senior, A hidden Markov model fingerprint classifier, in:Proceedings of the Thirty-First
Asilomar Conference on Signals,Systems & Computers, 1997, pp. 306–310.
[14] A. Ross, Information Fusion in Fingerprint Authentication, Ph.D.Thesis, Michigan State
University, 2003.
[15] D. Maio, D. Maltoni, R. Cappelli, J.L.Wayman, A.K. Jain, FVC2002:Second fingerprint
verification competition, in: Proceedings of the International Conference on Pattern Recognition
(ICPR), Quebec City, Canada, 2002, pp. 744–747.
[16] C. Hill, ―Risk of Masquerade Arising from the Storage of Biometrics,‖ master‘s thesis,
Australian Nat‘l Univ., 2001.
[17] Neurotechnology Inc., ―Verifinger,‖ http://www.neurotechnology.com/verifinger.html.
[18] R. Cappelli, A. Lumini, D. Maio, and D. Maltoni, ―Fingerprint Image Reconstruction from
Standard Templates,‖ IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 9, pp.
1489- 1503, Sept. 2007.
[19] S.O. Novikov and G.N. Glushchenko, ―Fingerprint Ridges Structure Generation Models,‖
Proc. SPIE Int‘l Workshop Digital Image Processing and Computer raphics, pp. 270-274, 1997.
[20] J.L. Araque, M. Baena, B.E. Chalela, D. Navarro, and P.R. Vizcaya, ―Synthesis of
76
| ACS-International Journal in Computational Intelligence, Vol-3,Issue-1 Mar 2012
Fingerprint Images,‖ Proc. 16th Int‘l Conf. Pattern Recognition, pp. 422-425, Aug. 2002.
[21] D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition.
second ed. Springer-Verlag, 2009.
[22] B.G. Sherlock and D.M. Monro, ―A Model for Interpreting Fingerprint Topology,‖ Pattern
Recognition, vol. 26, no. 7, pp. 1047-1055, 1993.
[23] J. Feng and A. K. Jain, ―Fingerprint reconstruction: from minutiae to phase,‖ IEEE Trans.
PAMI, 2010. [24] S. Yoon, J. Feng, and A. K. Jain, ―On latent fingerprint enhancement,‖ in
SPIE, vol. 7667, no. 766707, April 2010.
AUTHOR DETAILS
N.Kannaiya Raja received MCA degree from Alagappa University
and ME degree in Computer Science and Engineering from Anna
University Chennai in 2007 and he is pursing PhD degree in
Manonmaniam Sundranar University from 2008 and joinedassistant
professor in various engineering collages in Tamil Nadu affiliated to
AnnaUniversity and has eight years teaching experience his research
work in deep packetinspection. He has been session chair in major conference and workshops in
computer vision on algorithm, network, mobile communication, image processing papers and
pattern reorganization. His current primary areas of research are packet inspection and network.
He is interested to conduct guest lecturer in various engineering in Tamil Nadu.
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The Concrete Scheme of Pattern Matching In Reconstruction Fingerprint For Improving Accuracy
Dr.K.Arulanandam received Ph.D. doctorate degree in 2010 from
Vinayaka Missions University. He has twelve years teaching
experience in various engineering colleges in Tamil Nadu which are
affiliated to Anna University and his research experience network,
mobile communication networks, image processing papers and
algorithm papers. Currently working in Ganadipathy Tulasi’s Jain
Engineering College Vellore.
R. Somasundaram received degree B.Tech Information Technology
from Anna University Chennai in 2010. Now pursuing second year
ME Computer Science and Engineering in Arulmigu Meenakshi
Amman College of Engineering Kanchipuram affiliated to Anna
University Chennai.
‫ ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ‬IJCI-2K10-55 ‫ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ‬
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