a new framework for improving low quality fingerprint … a new framework for improving low quality...

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A New Framework for improving low Quality Fingerprint Images

JitendraChoudhary Dr.Sanjeev Sharma Jitendra Singh Verma

SOIT RGPV Bhopal SOIT RGPV Bhopal SOIT RGPV Bhopal Email: [email protected] Email: [email protected] Email: [email protected]

Abstract Fingerprints are the oldest and most widely used

form of biometric identification. A fingerprint image

may not always be well defined due to elements of

noise that corrupts the clarity of the ridge structures

or basic information, which is required for

recognition. Noise may occur due to variations in

skin and impression condition. Thus, image

enhancement techniques are often used to reduce the

noise and enhance the structure of ridges and valleys

for minutiae detection. in this paper, we present a

fingerprint image enhancement method which can

adaptively improve the clarity of ridge and furrow

structures of input fingerprint image based on the

frequency and spatial domain filtering , local

orientation estimation , local frequency estimation

and morphological operation. There set of operation

applied on own database DB-Finger that Improve the

quality of fingerprint Image.

1. Introduction

Fingerprint identification is one of the most important

biometrics technologies which have received

increasingly more attention recently. Both the

academic and industry developed their own

algorithms and techniques for fingerprint recognition.

Fingerprints are used in many applications such as

forensics, access control etc. A fingerprint

identification system plays vital role in any

identification systems due to its uniqueness and

persistence. the fingerprint images are not always

provided with good quality due to skin conditions

(wet or dry, cuts, and bruises), sensor noise, incorrect

finger pressure, and worn-off ridges fingers (elderly

people, manual workers). Minutiae points resulted in

either ridge ending or ridge bifurcation

[2].Fingerprint image with minutia showing in

figure.1The performance of fingerprint recognition

system is depends on the quality of fingerprint image

[10] [5]. Therefore, most of the efforts required for

improving the quality of fingerprint image.

Figure1. Fingerprint image with minutia showing.

The most common method use to acquire the

fingerprint image is to obtain the impression by

rolling an inked finger on paper and then scanning it

using flat bed scanner.

In this paper, a new Method for the Enhancement of

low quality fingerprint images is presented. It

includes pre-processing, some additional stages and

post-processing stages to enhanced Fingerprint

Images. The rest of the paper is organized as follows.

In section 2 related works; in section 3 the proposed

method is illustrated. The proposed algorithm is

shown in section 4, the experimental results based on

the proposed method are displayed in section 5 and in

section 6 the conclusion is presented.

2. RELATED WORK

1.K. V. Kale et.al, Fingerprint Image enhancement

techniques are often used to reduce the noise and

enhance the structure of ridges and valleys for

minutiae detection. In this paper propose a

composite method based work on the contrast

enhancement, frequency and spatial domain filtering,

and quick mask on gray scale images[7].

2.Lin Hong,et.al, In this paper present a fast

fingerprint enhancement algorithm, which can

adaptively improve the clarity of ridge and valley

structures of input fingerprint images based on the

estimated local ridge orientation and frequency [12].

3. HasanFleyeh, et. al, This paper presents a new

algorithm to segment fingerprint images. The

algorithm uses four features, the global mean, the

Jitendra Choudhary et al, Int. J. Comp. Tech. Appl., Vol 2 (6),1859-1866

IJCTA | NOV-DEC 2011 Available [email protected]

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local mean, variance and coherence of the image to

achieve the fingerprint segmentation. Based on these

features, a rule based system is built to segment the

image.

The proposed algorithm is implemented in three

stages; pre-processing, segmentation, and post

processing. Gaussian filter and histogram

equalization are applied in the pre-processing stage.

Segmentation is applied using the local features.

Finally, fill the gaps algorithm and a modified

version of Otsu thresholding are invoked in the post-

processing stage[1].

4. ShlomoGreenberg,et.al, In this work proposetwo

methods for fingerprint image enhancement. The first

one is carried out using local histogram equalization,

Wiener filtering, and image binarization. The second

method use a unique anisotropic filter for direct

grayscale enhancement. [8].

3. PROPOSED METHOD

Enhancement is a process for improving the

appearance or stability for particular image and its

applications. In fingerprint recognition system the

enhancement is an essential step for feature

extraction and matching. The overall performance of

the system is highly depends on quality of fingerprint

image i.e. good quality input image gives good

performance whereas poor quality image gives poor

performance. A fingerprint image enhancement

algorithm receives an input fingerprint image in pre-

processing stapes and applies a set of intermediate

steps on the input image, and the post-processing

steps, finally outputs the enhanced image. In order to

introduce our fingerprint image enhancement

algorithm, a list of notations and some basic

definitions are given below.

3.1 CONTRAST STRETCHING

The term spatial domain refers to the aggregate of

pixels used to compose the image. Spatial domain

methods are procedures that operate directly on the

pixels. Spatial domain process will be denoted by the

expression:

(1)

Where, f(x,y) is the input image, g is the

processed image and T is an operator on f defined

over some neighborhood of In addition T can

operate on a set of input images such as performing

the pixel by pixel some of k images for noise

reduction. T becomes a gray level operator also

called a gray level or mapping transformation

function [6] [5]. Through intensity mapping in our

experimental work we used the intensity stretching

technique using the gamma operator value 0.7.

3.2FREQUENCY AND SPATIAL FILTER

Frequency transformation discomposes an image

from its spatial-domain form of bright intensities into

a frequency domain form of frequency components

[9] as latent or fingerprint specialist are used to

dealing with a certain class of these images, we must

keep the subjective information brought by the

background. This background information is usually

removed by Appling Gaussian high-pass filter. The

foundation for linear filtering in both spatial and

frequency domain is the convolution theorem which

can be written as

(2)

and conversely,

(3)

Where, is the input image with filter

mask, F(u,v) is Fourier transform, and is

filter transfer function.In frequency domain filtering a

filter transform function modifies If high

frequency components of are attenuates and

low frequencies relatively unchanged then the

applied filter is lowpass filter.

High pass filter is defined as follows:

1-e-D(u,v)/2

(4)

Where, σ is the standard deviation, D(u,v) the

distance from point (u,v) to the center of the filter.

Fingerprint requires the enhancement of small areas

and details. A possible way of processing the image

is by considering not only the pixel itself, but also the

neighborhood of it at every location in the image [6] .

3.3 QUICK MASK EDGE DETECTOR

One of the principal approaches for edge detection is

based on the use of mask also called as filter, kernels,

templates, and windows. The quick mask is so named

because it can detect edges in all eight directions in

one convolution this as obvious speed advantages

when you want to detect all the edges [14].In the

experiment of this paper the quick mask of size 3-by-

3 is used to connect broken, cut and weak ridges of

all eight directions within only one convolution.



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Following 3X3 matrix are used as a Quick Mask in

our method

-1 0 -1

0 4 0

-1 0 -1

3.4 AVERAGE FILTER

The Average (mean) filter smoothes image data, thus

eliminating noise. This filter performs spatial filtering

on each individual pixel in an image using the grey

level values in a square or rectangular window

surrounding each pixel. Here we use 5X5 average

filter

1/25 1/25 1/25 1/25 1/25

1/25 1/25 1/25 1/25 1/25

1/25 1/25 1/25 1/25 1/25

1/25 1/25 1/25 1/25 1/25

1/25 1/25 1/25 1/25 1/25

3.5NORMALIZATION

The next step in the fingerprint enhancement process

is image normalization [12]. Normalization is used to

standardize the intensity values in an image by

adjusting the range of grey-level values so that it lies

within a desired range of values. Let represent

the grey-level value at pixel (i; j), and N(i; j)

represent the normalized grey-level value at pixel

. The normalized image is defined as:

0+ 2

2

(5)

Where M and V are the estimated mean and variance

of I(i; j), respectively, and M0 and V0 are the desired

mean and variance values, respectively.

Normalization does not change the ridge structures in

a fingerprint; it is performed to standardize the

dynamic levels of variation in grey-level values,

which facilitates the processing of subsequent image

enhancement stages.

3.6ORIENTATION FIELD ESTIMATION

The orientation field of a fingerprint image represents

the directionality of ridges [5]. Fingerprint image

typically divided into number of non-overlapping

blocks and an orientation representative of the ridges

in the block is assigned to the block based on

grayscale gradients in the block. The orientation field

of block (i, j ) is given by

= -1Vy (I,j)

Vx (I,j) (6)

i+w/2 i+w/2

x = 2

x2

y

u=i-w/2 u= i-w/2 (7)

i+w/2 i+w/2

y = 2

x2

y

u=i-w/2 V=i-w/2 (8)

where w is the size of block . The orientation field of

a typical fingerprint image is shown in Fig.3.6 (The

orientation field is overlapped with the original

fingerprint image).

Fig .3.6 Oriented window and x-signature.

3.7 RIDGE FREQUENCY IMAGE

The gray levels along ridges and valleys can be

modeled as a sinusoidal-shaped wave along a

direction normal to the local ridge orientation (see

Fig.2.5). Therefore, local ridge frequency is another

intrinsic property of a fingerprint image. Let G be the

normalized image and 2 be the orientation image,

then the steps involved in local ridge frequency

estimation are as follows:



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=

(9)

Therefore, the frequency of ridges and valleys can be

estimated from the x-signature. Let be the

average number of pixels between two consecutive

’

X

0

1 (11)

peaks in the x-signature, then the frequency,

is computed as: If no

consecutive peaks can be detected from the x-

signature, then the frequency is

l l

= ∑ ∑ l ’

u=- /2 v=- /2 (12)

Where is a two-dimensional low-pass filter with

unit integral and w l= 7 is the size of the filter.

3.8 REGION MASK

As mentioned early, a pixel (or a block) in an input

fingerprint image could be either in a recoverable

region or an unrecoverable region. Classification of

pixels into recoverable and unrecoverable categories

can be performed based on the assessment of the

shape of the wave formed by the local ridges and

valleys. In our algorithm, three features are used to

characterize the sinusoidal-shaped wave: amplitude

(α), frequency (β), and variance(γ).

be the x-signature of a block

centered at (i, j).

1 1

)2

i=1 i=1 (13)

3.9 GABOR FILTERING

Apply filters to enhance the ridge pattern; once the

ridge orientation and ridge frequency information has

been determined, these parameters are used to

construct the even-symmetric Gabor filter. A two

dimensional Gabor filter consists of a sinusoidal

plane wave of a particular orientation and frequency,

modulated by a Gaussian envelope [19]. Gabor filters

are employed because they have frequency-selective

and orientation-selective properties. The even-

symmetric Gabor filter is the real part of the Gabor

function, which is given by a cosine wave modulated

by a Gaussian .An even symmetric Gabor filter in the

spatial domain is defined as [16]:

1

=exp-

2

(14)

(15)

Where is the orientation of the Gabor filter, f is the

frequency of the cosine wave, and yare the

standard deviations of the Gaussian envelope along

the x and y axes, respectively, and and define

the x and y axes of the filter coordinate frame,

respectively.

3.10 BINARISATION

Most minutiae extraction algorithms operate on

binary images where there are only two levels of

interest: the black pixels that represent ridges, and the

white pixels that represent valleys. Binarisation is the

process that converts a grey level image into a binary

image. This improves the contrast between the ridges

and valleys in a fingerprint image, and consequently

facilitates the extraction of minutiae. One useful

property of the Gabor filter is that it has a DC

component of zero, which means the resulting

filtered image has a mean pixel value of zero.



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3.11 THINNING

The final image enhancement step typically

performed prior to minutiae extraction is thinning.

Thinning is a morphological operation that

successively erodes away the foreground pixels until

they are one pixel wide. A standard thinning

algorithm [17] is employed, which performs the

thinning operation using two sub iterations.

4. Flow chart of proposed method

The flowchart of the fingerprint enhancement

algorithm is shown in Fig 6. The main steps of the

Method include:

STEP1: Pre- processing

(a)Read the input fingerprint Image I(x,y) from

DB-finger Database.

(b)Apply the Contrast Stretching to the Input Image.

STEP2:

Compute the discrete Fourier transform of the

Intensity mapping image.

STEP 3:

Convolve I(u,v) with Gaussian High pass filter

transform function H(u,v).

STEP 4:

Compute the Inverse discrete Fourier transform.

STEP 5:

(a) Apply the 3x3 quick mask edge detector to the

inverse DFT image.

(b) Apply the 5x5 average filter using convolution for

smooth the ridges.

STEP 6:

(a) An smooth image is normalized.

(b) The orientation image is estimation for the

normalized image.

STEP 7:

The frequency image is computed for the estimated

orientation image

STEP 8:

(a) Region mask is obtained by classifying each

block in the normalized input fingerprint image.

(b) Apply the Gabor filter which is tuned to local

ridge orientation and ridge frequency is applied to the

ridge-and –valley pixels.

STEP 9:Post- processing

(a) Convert the image into binary image.

(b) Apply the morphological thinning operation for

obtaining the thinned ridge line.

STEP 10:

Finally obtains the enhanced fingerprint image.

I(x,y)

Input Image

g(x,y)

Enhanced Image

Fig. 4: Block Diagram of Proposed Method

Pre-processing

Discrete Fourier transform

Convolution with High Pass

Filter

Inverse Discrete Fourier

Transform

Quick Mask Edge Detector

Average Filter

Normalization

Local orientation Estimation

Local Frequency Estimation

Region Mask Estimation

Post-processing

Gabor Filtering



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5. Experimental result

The proposed Enhancement algorithm is tested on

100 fingerprint images which are selected randomly

and without repetition from database DB-finger to

evaluate the efficiency of this algorithm; human

expert examines the results of the Enhancement

algorithm from these random images. Figure 5shows

a number of successful Enhanced achieved by this

algorithm.

Fi

g. 5: Correctly Enhanced images from

Own database DB-finger.

In order to evaluate this enhancement method

quantitatively, a four-level scheme is suggested. The

number of correctly Enhanced blocks in the image is

measure on which this scheme is based. This scheme

is defined as follows:

• Good, when more than 80% of the fingerprint

Images are enhanced correctly.

• 12% of the fingerprint Images are Almost Good

• Almost Bad, when 5% of the fingerprint Images are

not correctly enhanced.

• Bad, when 3% of the fingerprint Images are not

enhanced due to excessive noise.

The results of classification into four categories are

shown in Figure 6.

Good

Almost good

Almost bad

Bad

Fig. 6: Four samples from own DB-finger database

which show the good, almost good, almost bad and

bad categories.



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This scheme is applied on the tested images extracted

from OwnDatabase DB-finger and the results of This

Algorithm are depicted Table I. According to this

scheme, Good and Almost Good results can give

82% of the images under test.

Table I: Results of different categories:

Result

Percentage

Good

80 %

Almost Good

12 %

Almost Bad

05 %

Bad

03 %

Chart: Result of Different Categories of Fingerprint

Images:

6. Conclusion In this paper we have proposed a method for

enhancement of noisy fingerprint images. We have

applied our method on a database of 100 images. The

results show that 80-92% images are enhanced

successfully. Remains 8-20% images were not

successfully enhanced due to excessive noisy.

This proposed method shows the good performance

for enhancement as correcting the broken ridges in

noisy images.

7. References [1]Hasanfleyeh, dialasomaa, mark Dougherty “

segmentation of low quality fingerprint images” IEEE2010.

[2] C. T. Tung, “Fingerprint Image Enhancement Based on

Teager Operator in The Wavelet Domain”, Master thesis, Computer Science and Information Engineering, National

Central University, 2006.

[3] Rafael C. Gonzalez, Richard E Woods and Steven L

Eddins "Digital Image Processing using MATLAB", Pearson Education, 2004.

[4]D.Maltoni, D.Maio, A.K.Jain and S.Prabhakar ,Hand

Book of fingerprint recognition, springer ,newyork,2003.

[5] D. Maltoni, Dmaio, A.K. Jain, S. prabhakar, "Hand book of Fingerprint Recognition", springer, 2003.

[6] Rafael C. Gonzalez, Richard E Woods, "Digital Image

Processing", Second Edition, Pearson Education, 2003.

[7]K.V.Kale, R.R.Manza, V.T.Humbe, PraptiDeshmukh” fingerprint image enhancement using composite

technique”f-14-14/2003(SR)

[8]Shlomogreenbrg, mayealadjem,DanielKogan and

ItshakDimitrov “fingerprint image enhancement using filter technique”07695-0750-6100,IEEE 2000

[9] John C. Russ, "The Image Processing Handbook", 3rd

edition CRC Press 1999.

[10] Ling Hong, Yifei Wan, A.K. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation",

IEEE, Tran. On Pattern Analysis and Machine Intelligence,

vol. 20.pp. 777- 789, Aug-1998.

[11]Ling Hong, Yifei Wan, A.K. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation",


vol. 20.pp. 777-789, Aug-1998.

[12] Ling Hong, Yifei Wan, A.K. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation",


vol. 20.pp. 777-789, Aug-1998.

[13]K. Karu and A.K. Jain, “Fingerprint Classification,” Pattern Recognition, vol. 29no. 3, pp. 389-404, 1996.[14] Dwayne Phillips, "Image Processing in C", BPB Publications, Ist Indian edition 1995. [15] A.K. Jain and F. Farrokhnia, “Unsupervised Texture

Segmentation Using Gabor Filters,” Pattern Recognition,

vol. 24, no. 12, pp. 1,167- 1,186, 1991.

[16] Jain, A. K., and Farrokhnia, F. Unsupervised texture segmentation using Gabor filters. Pattern Recognition 24,

167–186, 12 (1991).

[17] Guo, Z., and Hall, R. W. Parallel thinning with two-

sub iteration algorithms. Communications of the ACM 32, 359–373,3 (March 1989).

[18] J.G. Daugman, “Uncertainty Relation for Resolution

in Space, Spatial-Frequency, and Orientation Optimized by

Two- Dimensional Visual Cortical Filters,” J. Optical Soc. Am., vol. 2, pp. 1,160-1,169, 1985.

[19] Daugman, J. G. Uncertainty relation for resolution in

space, spatial frequency, and orientation optimized by two-

dimensional visual cortical filters. Journal of the Optical

Society of America (A) 2, 7 (July 1985), 1160–1169.



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a new framework for improving low quality fingerprint … a new framework for improving low quality...

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