The Concrete Scheme of Pattern Matching In Reconstruction

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 Raja 1 , K.Arulanandam

2 , and R. Somasundaram 3

1.A.P/CSE Dept. Arulmigu Meenakshi Amman College of Engg, Thiruvannamalai Dt, near Kanchipuram Email: kanniya13@hotmail.co.in

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: somsb88@gmail.com

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


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.



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


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


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).


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


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


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, fc-i

(1)

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 fc th minutia that makes the closest minutia direction dominates the ridge orientation of neighboring blocks.

The orientation at block (m, n) is then computed as:



We also consider that singular points have been marked in some of the latent fingerprints. In those cases, the direction field of N s singular points is given by the Zero-Pole model where m si

, n si

, and si

(core: 1, delta: 1) are the location and type of the i th

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,


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


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

70

(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 = {(x i

, y i

,u i

)}N i

=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 orientation field f from set independently and finally combined to get the

Where tan21 is a four quadrants arctangent function.



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 f c

(x, y) = cos(2f (x, y)) and f s

(x, y) = sin(2f (x, y) to address the singularity problem of orientation. F c

and f s 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


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


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

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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.



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.

ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ IJCI-2K10-55 ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ



The Concrete Scheme of Pattern Matching In Reconstruction

The Concrete Scheme of Pattern Matching

In Reconstruction Fingerprint For

Improving Accuracy

ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ IJCI-2K10-55 ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ

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The Concrete Scheme of Pattern Matching In Reconstruction

The Concrete Scheme of Pattern Matching

In Reconstruction Fingerprint For

Improving Accuracy

ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ IJCI-2K10-55 ﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣﻣ

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