Enhancement and Minutiae Extraction Of Touchless Fingerprint Image Using Gabor And Pyramidal Method

8/7/2019 Enhancement and Minutiae Extraction Of Touchless Fingerprint Image Using Gabor And Pyramidal Method

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Enhancement and Minutiae Extraction of

Touch less Fingerprint Image Using Gabor

and Pyramidal Method

A.John Christopher

Associate Professor, Department of Computer Science,

S.T. Hindu College, Nagercoil

Abstract - Touch based sensing techniques generate lot of errorsin fingerprint minutiae extraction. The solution for this problem

is touchless fingerprint technology. They do not receive any

contact between the sensor & finger. Although they reduce the

problems of touch based finger prints, other difficulties explore

such as a view difference problem and a limited usable area dueto perspective distortion. To solve this problem, proposed

method for touchless fingerprint image enhancement andminutiae extraction is introduced. Image enhancement is mostly

required preprocessing system for finger based biometricsystem. Normally the touchless device is having a single camera

and two planer mirrors which reflecting side views of a finger.From this we get three images normally frontal, left and right

finger. Experimental result shows that the enhanced imagesincrease the biometric accuracy.

Index Terms - pyramidal method, Gabor, touchless fingerprint,

thinning, normalization, finger enhancement, adaptive histogram.

I ‐ INTRODUCTION

A fingerprint is composed of ridges and valleys.

Ridges have various kinds of discontinuity such as ridgebifurification, ridge endings, short ridges, islands and ridge

cross over’s. Among this discontinuity, ridge bifurification

and ridge ending are commonly used in fingerprint

identification/verification system and are called minutiae

[1].For the processing of fingerprint images, two stages are of pivotal importance for the success of biometric

reorganization: image enhancement and minutiae extraction.

The traditional fingerprint processing technologies areapplied immediately after sensing. But a better thing is an

optional image enhancement in fingerprint images. In

realistic scenarios though the quality of a fingerprint image

may suffer from various impairments, caused by scores, cuts,moist or dry skin, sensor noise, blur, wrong handling of

sensor, weak ridge and valley pattern of the given fingerprint,etc. The task of the fingerprint enhancement is to counteract

the aforesaid quality impairments and to reconstruct the

actual fingerprint pattern as trace to it original as possible. [2]Fingerprints are traditionally captured based on contact of the

finger on paper or a platen. This often results in partial or

degraded images due to improper finger placement, skin

Dr.T.Jebarajan,

Principal,V.V. College of Engineering., Tisayanvilai

deformation, slippage, smearing or sensor noise. Some of the

touch based are shown in fig.1. A new generation of

touchless live scan devices that generate three various

representation of fingerprint is appearing in the market. Thisnew sensing technology addresses many of the problems

stated above [3]. From wear and tear of surface coating, toovercome these kinds of problems, a touchless fingerprint

sensing technology has been proposed that does not require

any contact between a sensor and a finger. Thus, the fingers

and ridge information cannot be changed or distorted as it

will be free of skin deformation. Also, it can capturefingerprint images consistently because it is not affected by

different skin conditions or latent fingerprints.

Fig. 1: Distorted images acquired from a touch-based sensor.

Recently, several companies and research groups have

developed touchless fingerprint sensors and recognition

systems [4]–[6]. TST Group developed a touchless imagingsensor (BiRD III) which uses a complementary metal–

organic–semiconductor (CMOS) camera, and red and green

light sources to acquire fingerprint images [4]. Song et al. [5]

proposed a sensing system with a single charged-coupleddevice (CCD) camera and double ring-type blue illuminators

to capture high contrast images. Also, Mitsubishi Electric

Corporation proposed another touchless approach

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 9, No. 3, March 2011

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transmitting the light through the finger [6], acquiring

fingerprint patterns under the surface of skin using light witha wavelength of 660 nm. However, such sensing systems [4]–

[6] have an inherent problem as they use only a single

capturing device, such as CMOS or CCD cameras. whencapturing an image using a single camera, the geometrical

resolution of the fingerprint image decreases from the

fingerprint center towards the side area [7]. Therefore, falsefeatures may be obtained in the side area and it reduces the

valid and useful region for authentication. Moreover, if there

is a view difference between images due to finger rolling, itreduces the common area between fingerprints and degrades

system performance. To solve this problem, 3-D touchlesssensing systems using more than one view have been

explored [8]–[11]. TBS [8] used five cameras placed around

a finger to capture nail-to-nail fingerprint images and

generated a 3-D fingerprint image using the shape-from-

silhouette method. They then unwrapped the 3-D finger

image onto a 2-D image by using parametric and

nonparametric models to make rolled-equivalent images [9].

Fatehpuria et al. [10] proposed a 3-D touchless device using

multiple cameras and structured light illumination (SLI). Thestructured light patterns are projected onto a finger to obtain

its 3-D shape information and 2-D unfolded images are

generated by applying “Springs algorithm” and some post

processing steps. Also, the Hand Shot ID system wasdeveloped to acquire a 3-D shape of a hand with fingers by

stitching images from 36 cameras [11]. Although all thesemethods attempted to solve the problems in touch-based

sensors and acquire expanded fingerprint images with less

skin deformation, they did not raise much interest in themarket because of much higher costs compared to

conventional touch-based sensors. Considering the above

observations, we adopt a new touchless sensing scheme using

a single camera and a set of mirrors. The mirrors work as

virtual cameras, thus enabling the capture of an expandedview of a fingerprint at one time without using multiple

cameras. The device consists of a single camera, two planar

mirrors, light-emitting diode (LED)-based illuminators, and a

lens. Two planar mirrors are used to reflect the left and rightside view of a finger. In this paper, we proposed a new

method to enhance the touchless finger print and to extract

the minutiae data.

II – SYSTEM DESIGN

To overcome the view difference problem and thelimitation of a single view, some touchless fingerprinting

systems capture several different views of a finger by using

multiple cameras. However, using multiple cameras increases

the cost and size of a system. Thus, we adopt a new sensingsystem which captures three different views (frontal, right,

and left) at one time by using a single camera and two planar

mirrors. Figs. 2(a) and (b) show the prototype and schematicview of the device. As shown in Fig. 2, two mirrors are

placed next to the finger and reflect the right and left side

views of the finger. Then, the frontal view and two mirror-

reflected views are captured by a single camerasimultaneously. A mirror-reflected image is regarded as the

“flipped” image taken by a virtual camera placed at a

different direction compared to the real one. Therefore, we

can capture three different views of a fingerprint using only

one Camera and also avoid the synchronization problem

existing in multiple camera-based systems. In addition, to

obtain high-quality fingerprint images, we need to consider

several optical components in order to design the device.

Fig. 2: Proposed device.

(a) Prototype of the device. (b) Schematic view of the device.

The specifications of the optical components are as follows:

1) Camera and lens: We use a 1/3-in progressive scan

type CCD with 1024 x 768 active pixels, where the

pixel size is 4.65 x 4.65 m. This camera offers asufficient frame rate of 29 Hz, thus avoiding image

blurring caused by typical finger motion. Also, weuse simple equations [see (1) and (2)] to design an

adequate lens for our system.

qM

p= (1)

1 1 1

f p q= + (2)

Where f is the lens focal length, p and q are the lens-

to-object and lens-to-image distances, respectively,and M is the optical magnification. Normally, the

required image resolution for touch-based sensors is500 dpi. Therefore, to ensure a 500-dpi spatial

resolution in the fingerprint area and to cover three

view fingerprints, the optical magnification

parameter M, the lens to image distance, and field of

view (FOV) are determined as 0.1, 170 mm, and 50x 38 mm, respectively. By doing this, we can

capture three view images with 500-dpi resolution at

one time. Also, the depth of field (DOF) of the lens

ranges from -2.6 to +2.6 mm at a given workingdistance and it normally covers the half depth of a

finger.

2) Illumination: Considering the reflectance of human

skin to various light sources, we used ring-shaped

white LED illuminators and a band pass filter which

can transmit green light to enhance the ridge-to-

valley contrast. Also, the illuminators are placedperpendicular to the finger to remove the shadowing

effect. Diffusers are used to illuminate a finger

uniformly.



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Fig. 3: Overall flowchart of the proposed method

3) Mirror: Two planar mirrors are positioned next to

the left and right side of the finger and the mirror

size is determined to cover the maximum thumbsize. To provide enough overlapping area between

frontal- and side-view images, the angles of the

mirrors are determined 15 empirically. Also, the

mirrors can be used as pegs to place a user’s finger firmly on the device.

III – PROPOSED METHOD In this section, we explain the Enhancement method for

synthesizing an expanded fingerprint image from frontal- andside-view images. The overall scheme of the method is

presented in Fig. 3 The method is mainly composed of six

stages (foreground separation, normalisation, Gabor filtering,

pyramidal method, thinning, minutiae extraction). Inforeground separation we will do the morphological

operation, in normalisation we pre-process the image etc.

A) Foreground separation Using morphological operation we use the erosion

followed by dilation, this can be done up to required

time. Mathematical morphology is a method of

processing digital images on the basis of shape. Adiscussion of this topic is beyond the scope of this

manual. A suggested reference is: Haralick,

Sternberg, and Zhuang, "Image Analysis Using

Mathematical Morphology," IEEE Transactions on

Pattern Analysis and Machine Intelligence, Vol.PAMI-9, No. 4, July, 1987, pp. 532-550. Much of

this discussion is taken from that article. Briefly, the

DILATE function returns the dilation of image bythe structuring element Structure. This operator is

commonly known as "fill", "expand", or "grow." It

can be used to fill "holes" of a size equal to or

smaller than the structuring element. Used with

binary images, where each pixel is either 1 or 0,

dilation is similar to convolution. Over each pixel of

the image, the origin of the structuring element isoverlaid. If the image pixel is nonzero, each pixel of

the structuring element is added to the result using

the "or" operator. Used with greyscale images,which are always converted to byte type, the

DILATE function is accomplished by taking the

maximum of a set of sums. It can be used toconveniently implement the neighbourhood

maximum operator with the shape of the

neighbourhood given by the structuring element.Used with greyscale images, which are always

converted to byte type, the ERODE function is

accomplished by taking the minimum of a set of differences. It can be used to conveniently

implement the neighbourhood minimum operator

with the shape of the neighbourhood given by the

structuring element.B) Normalisation

The process of removing the effects of the sensor

noise and gray-level background due to finger

pressure differences. The objective of this stage is

decrease the dynamic range with gray scale betweenridges and valleys of the image. Normalization

factor is calculated according to the mean and thevariance of the image. Each and every pixel in the

fingerprint image has to be processed to find the

median value. The average value of all the pixels is

calculated i.e, the median value. By comparing the

median value with the current pixel the replacementcan be performed.

Normalization facilitates have the subsequent

processing steps.Let G (i, j) denote the normalized gray-level value at

pixel (i, j). The normalized image is defined as

follows:

(3)

Where, 0M and 0VAR denote the desired

mean and variance value, respectively.

Most fingerprint images on a live-scan input device

are usually of poor quality. The fingerprint image is

smoothed with an average or median filter.

C) Gabor filtering

A Gabor filter is a linear filter used in imageprocessing for edge detection. Frequency and

orientation representations of Gabor filter are similar

to those of human visual system, and it has been

found to be particularly appropriate for texture

representation and discrimination. In the spatialdomain, a 2D Gabor filter is a Gaussian kernel

function modulated by a sinusoidal plane wave. The

Foreground separation

Gabor filtering

Normalization

Pyramidal method

Thinning

Minutiae extraction




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Gabor filters are self-similar - all filters can be

generated from one mother wavelet by dilation androtation. Its impulse response is defined by a

harmonic function multiplied by a Gaussian

function. Because of the multiplication-convolutionproperty (Convolution theorem), the Fourier

transform of a Gabor filter's impulse response is the

convolution of the Fourier transform of theharmonic function and the Fourier transform of the

Gaussian function.

),,,,;,( γ σ θ λ yxg

(4)

Where ' cos sinx x yθ θ = + and

' sin cosy x yθ θ = − +

In this equation, λ represents the wavelength of thecosine factor, θ represents the orientation of the

normal to the parallel stripes of a Gabor function, φ

is the phase offset, σ is the sigma of the Gaussianenvelope and γ is the spatial aspect ratio, and

specifies the ellipticity of the support of the Gabor

function.

D) Pyramidal method

Pyramid decomposition requires resizing(scaling, or other geometric transformation). To

create our Gaussian and Laplacian like pyramids, we

define the reduce(I,K) and expand(I,K) operations,

which decrease and increase an image in size by thefactor K, respectively. During reduce, the image is

initially low-pass filtered to prevent aliasing using a

Gaussian kernel.2. The latter’s standard deviationdepends on the resizing factor, which here follows

the lower bound approximation of the corresponding

ideal low-pass filter . We initially

reduce the original fingerprint image FP by a factor

of in order to exclude the highest

frequencies. In a further step, we

Table - 1Pyramidal building process

a) Pyramidal decomposition

Gaussian-like Laplacian-like

G1=reduce(fp,k0)

G2=reduce(g1.k)

L1=g1-expand(g2,k)

L2=g2-expand(g3,k)

Reduce the image size by a factor k for three times.

This is also outlined on the upper left hand side of Table1. To create images containing only band limited signals

of the original image, we expand the three images by

factor and subtract each of them from the next lower

level.E) Thinning

The THIN function returns the "skeleton" of a bi-level image. The skeleton of an object in an image is

a set of lines that reflect the shape of the object. The

set of skeletal pixels can be considered to be themedial axis of the object. For a much more

extensive discussion of skeletons and thinning

algorithms, see Algorithms for Graphics and ImageProcessing, Theo Pavlidis, Computer Science Press,

1982. The THIN function is adapted from Algorithm

9.1 (the classical thinning algorithm).On input, thebi-level image is a rectangular array in which pixels

that compose the object have a nonzero value. All

other pixels are zero. The result is a byte type imagein which skeletal pixels are set to 2 and all other

pixels are zero.F) Minutiae extraction

A feature extractor finds the ridge endings and ridge

bifurcations from the input fingerprint images. If ridges can be perfectly located in an input

fingerprint image, then minutiae extraction is just a

trivial task of extracting singular points in a thinnedridge map. However, in practice, it is not always

possible to obtain a perfect ridge map. The

performance of currently available minutiae

extraction algorithms depends heavily on the qualityof the input fingerprint images. Due to a number of

factors (aberrant formations of epidermal ridges of

fingerprints, postnatal marks, occupational marks,

problems with acquisition devices, etc.), fingerprintimages may not always have well-defined ridge

structures. A reliable minutiae extraction algorithm

is critical to the performance of an automaticidentity authentication system using fingerprints.

Fig. 4: Types of Ridge Patterns

Fig. 5: Minutiae points




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Minutiae are extracted from the thinned image by

using the Crossing Number algorithm.

(5)

Where Pi 0 or 1 in the 3*3 Neighbor of P

Characteristic of CN

CN Character

0 Isolated point

2 End point

4 Bifurcation point

IV – EXPERIMENTAL RESULTS For the experimental results we acquired 100 set of finger print images, each set contain frontal, left and right view

images. One of the used images set is shown in the Fig: 6 and

the enhanced image is also shown in the Fig: 7. The minutiae

extraction results also expressed in Fig: 8. The most definite

indicator of touchless image quality is the number of trueminutiae additionally extracted.

Fig. 6: Input images

Fig. 7: Enhanced images

Human experts prove that the more true minutiae extractedfrom the enhanced image. The touchless fingers are better

than the conventional touch based fingers, that conclusion

can be deviate from the results. The finger print quality

checking methods compare the foreground size of the fingers.

Here foreground means the good quality regions of the finger print. The foreground size measures are tabulated as follows:

Fig. 8: Minutiae

Table - 2Average increasing rate of Foreground size in terms of each measurement

Quality measurement Average increase rate of

foreground size

Standard deviation [12] 28.65%

Coherence [13] 33.72%

Gradient – based

method [14]

30.81%

However we can expect that our enhanced image can be

making high performance when view difference image are

matched. The Table-2 shows the result of our enhancedimage.

V – CONCLUSIONS AND FUTURE WORK

This paper proposes a new method for touchless fingerprintsensing images. To get the better minutiae extraction, the

three fingerprints (frontal, left, right) are enhanced usingGabor and pyramidal method. For experimental results, the

enhanced fingerprints are having better enhanced ridges and

the valleys. Also minutiae extraction is handled. The results

are analysed and described in tables and graph format. In thispaper we limits the research work up to minutiae extraction,

this research can be continued on mosaicing of the threeenhanced images. Feature work can be done on the same

concept. According to the result, it is concluded that the

proposed system generate better enhancement on touchless

fingerprint than the existing methods.

REFERENCES

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[2] Hartwig Fronthaler, Klaus Kollreider, and Josef Bigun ,LocalFeatures for Enhancement and Minutiae Extraction in

Fingerprints, IEEE Transactions On Image Processing , VOL. 17,

NO. 3, MARCH 2008[3] Yi Chen1, Geppy Parziale2, Eva Diaz-Santana2, and Anil K Jain,

“3d Touchless Fingerprints: Compatibility With Legacy Rolled

Images” Michigan State University Department of Computer

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[4] TST Group Aug. 03, 2009 [Online]. Available: http://www.tst-

biometrics.com




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[5] Y. Song, C. Lee, and J. Kim, “A new scheme for touchlessfingerprint recognition system,” in Proc. Int. Symp. Intelligent

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[6] Mitsubishi Touchless Fingerprint Sensor Aug. 03,

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[Online].Available: http://www.tbsinc.com/[9] Y. Chen, G. Parziale, E. Diaz-Santana, and A. K. Jain, “3D

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[10] A. Fatehpuria, D. L. Lau, and L. G. Hassebrook, “Acquiring a 2-

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[11] Aug. 03, 2009 [Online]. Available:

http://privacy.cs.cmu.edu/dataprivacy/ projects/handshot/index.html

[12] L. Hong, Y.Wan, and A. K. Jain, “Fingerprint image

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Aug. 1998.[13] E. Lim, X. Jiang, and W. Yau, “Fingerprint quality and validity

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Enhancement and Minutiae Extraction Of Touchless Fingerprint Image Using Gabor And Pyramidal Method