Fingerprint Enhancement Using Frequency Domain Filter

Anavil Mishra

ME Scholar (ECE)

NITTTR , Chandigarh

Rohini

ME Scholar (ECE)

NITTTR , Chandigarh

In the present paper we will discuss about

fingerprint image enhancement for fingerprint recognition. Many experiments have been

done in past for fingerprint image enhancement. Many new procedures &

advances have developed gradually which has improved the poor quality

fingerprint images in a remarkable way. Therefore, in the given paper, an

assessment of these procedures & advances are done with its rationale

points to validate the results. Based on the given procedure, a consolidated

solution for fingerprint identification is evolved for affirmation. For the plan of action some methods at the time of coding &

calculations are initiated to improve & upgrade the working of this

fingerprint recognition system. The results are proposed only after conducting

multiple experiments on variety of fingerprint images

Keywords: Biometrics, Image processing, Fast Fourier transform, Gaussian

Band pass Filter, signal

processing, image analysis, feature extraction, fingerprint image enhancement,

minutia matching.

1.

Introduction

BIOMETRICS is one of the most reliable solutions to determine identity

of an individual by identifying their physiological or behavioral

characteristics. Some of the commonly used physiological characteristics and

behavioral for biometric recognition are face, hand & fingerprints, iris,

retina, DNA, signature, voice etc. Automated fingerprint identification system

(AFIS) is the most commonly used technique implemented across the globe. It has

therefore, now become most important in security field because of which it has

interested many researchers to continue doing their research on it. Every human

being have different patterns of crest (ridges) & troughs (valleys). The

image which is shown in Fig.1 is showing the surface of the fingerprint which

is taken where the dark lines are showing the ridges and the light lines in the

fingerprint image is showing the valley.

Fig. 1. Ridge and Valley Structure 1

Mainly, three standards about fingerprint are

mentioned below:

1.1

Unalterable: Fingerprint patterns are permanent and never alter during the

growth of human body.

1.2.

Unique: Every human being has unique fingerprint ridges & are

never identical in two different people.

1.3.

Grading: Grading of fingerprint patterns is limited. This makes the

logical grading of fingerprint pattern easier.

Images of fingerprint which are

obtained from people for identification purposes are not often clear and

sometimes quality is also not perfect. Different factors like scars, sometimes moisture

which is present in scanner and there are also many more reasons which are

responsible for affecting the quality of an image which can lead to failure or

maybe there are chances of false extraction of minutiae.

Hence, it has become necessary to

adapt techniques for image enhancement to reduce noise present in it and also

to improve statement of ridges against valleys. This paper contains, various

fingerprint image enhancement techniques and these are discussed. The outcome result

of these techniques are shown in the given Table 1 and results of these

techniques are also discussed in Section 5. This further propose the fingerprint

image enhancement in two different domains that are spatial domain &

frequency domain which are discussed. Clarity

of fingerprint image and contrast of different fingerprint images are improved

by using technique that is the spatial domain technique and then Gaussian

Bandpass filter is used which is tuned to a

fixed range of frequencies to transform them to the different fourier image. Resultants are experimented on original

samples of fingerprints by using filters to further analyze the frequency

domain which is generated directly.

2.

PROPOSED WORK

& BACKGROUND

In this section, to improve the

clarity of ridges & valleys of the fingerprint impression some calculations

& methods are initiated that have been initiated to improve the picture

clarity of ridges and valleys. In this Frequency Domain Filter is used which

allows the fingerprint images to combine with the large size filters

efficiently.

For the improvement of image

Gabor filters have been used as initiated in 7, 8, 9, 10. To use Gabor

filters, four different parameters are

used that are local ridge orientation, local ridge frequency and the standard

deviation of the Gaussian envelop along the x-axis and y-axis (?x and ?y) must be

selected specifically to avoid false ridges & valleys. For other

fingerprint enhancement techniques the methodology has been explained in

Section 3 & the results of the methodology implemented is given in Section

5.

3. FINGERPRINT IMAGE

ENHANCEMENT TECHNIQUES

The following techniques have been used to analyse

& enhance the fingerprint images:

3.1. Short Time Fourier

Transform (STFT) Analysis:

In 2 3, as a

new technique STFT analysis has been introduced for fingerprint magnification.

Fig.2 below shows the audit of the initiated methodology.

Original

image

Enhanced Image

Fig. 2. Initiated methodology in 2

Based

on the contextual filtering a new fingerprint image enhancement algorithm in

the Fourier domain is presented. In Fig. 1, input image is divided into

projected windows during STFT analysis. It is presumed that the image within

this small window is soaked and can be modelled as a surface wave. The rough

calculations of ridge frequency and ridge orientation are then acquired. For the computation of angular coherence the

energy map yielded from STFT analysis is used. This coherence image is used to

adapt to the angular bandwidth. Then, the resultant contextual information is

used to filter each window on the Fourier domain.

table

The

above calculated table can be summarised as under:

It

has two stages. The first stage is composed of STFT analysis and second stage

is composed of contextual filtering. The STFT stage furnishes the three images

-ridge orientation, ridge frequency and the block energy. These are further

utilised to calculate the region mask. This is almost identical to the

technique which is initiated in 4. Hence, all the intrinsic images which are

required to perform contextual filtering are produced out of the analysis phase.

3.2. Pyramid-based Filtering

This technique uses the

familiar methods for improvement and for drawing particulars as initiated in

5. Two types of symmetries namely parabolic and linear are used to prototype

as an example & draw the confined structure in a fingerprint image &

these symmetries can be roughly calculated by separate filtering of the

orientation tensor. In the improvement stage, a Laplacian-like image pyramid is

used to fragment the original fingerprint image into sub bands correlating to

different spatial scales.The frequency directions originate from the frequency

structures where surround smoothing is done on the pyramid at different levels.

In Fig. 4, the reduction (I, k) and expansion (I, k) function are defined to

create the Gaussian & Laplacian pyramid. The function is shown below in

Fig. 4.

a) Pyramid Decomposition PD

a)

Gaussian-Like

Laplacian-Like

g1 = reduce (fp, ko);

g2 = reduce (g1,k);

g3 = reduce (g2,k);

g4 = reduce (g3,k);

l1 = g1- expand(g2,k);

l2 = g2-expand(g3,k);

l3 = g3- expand(g4,k)

b)

Reconstruction R

fp = expand (……ko);

expand (l3,k) +l2

Fig. 4. Pyramid building process

in 5

Later

to detect the accurate position & direction of the minutia the parabolic

symmetry is added to the local fingerprint model. The parabolic symmetry is

further reduced if the linear symmetry is high whereas it is maintained vice

versa. All these steps are followed in compliance with extracting the finer

details.

3.3. Curved Gabor Filters

The

Gabor Filters play a crucial role in the improvement of different types of

images & in the process of drawing Gabor features. In 6, the curved Gabor

filters were introduced purposely to upgrade the curved structures in noisy

images. The choice of filter parameters increases the smoothing power of the

magnified images without fabrication. In several

operations of image processing and pattern realization, Gabor functions 8, in

the form of Gabor filters 9 and Gabor wavelets 10 were enforced for various

functions

In

6, it is observed that curved Gabor filter is applied to the curved ridge

& valley structure to enhance the poor quality fingerprint images.

Initially, two orientation field estimation methods were combined to obtain

more sturdy estimations for fingerprint images which are extremely noisy in

nature.

Curved

Gabor filters allow the use of large curved regions without creating false

features. It is therefore a testified & proven fact that the curved Gabor Filters draw

better results in comparison to the traditional Gabor Filters.

Fig. 5. (Left): Original

Fingerprint Image, (Middle): Image drawn after the use of Traditional Gabor

filter, (Right): Image drawn after the use of Curved Gabor filter.

3.4. Fast

Fourier Transform and Gaussian Bandpass Filters

Have been used to magnify

the fingerprint images.

3.4.1. The subdivided image is further divided into

non converging blocks of size W x W as presented in 7, The total time taken

to calculate FFT is much faster when the block sizes are power of two8.Thus,

16 X 16 is suggested to use for computation of value.

3.4.2. Further for every block, 2D Discrete Fast

Fourier Transform is suggested to apply based on 1. The value F(0,0) of the

DFT is called the DC coefficient. When u=v=0 is substituted in the definition

given in 1, then

F(0,0) = ? M-1 ? M-1 f(x,y) exp(0 ) = ? M-1 ? M-1 f(x,y)

(2)

X=0 y=0

x=0 y=0

The Fourier

transform is complex. If the real components are represented by R(u,v) and

Imaginary components are represented by I (u,v) then the Fourier range will be

defined as:

=

To envision, it is appropriate to have

zero frequency element DC in the center of the matrix 8. In comparison to

other values DC coefficient is larger. Fourier transform Fingerprint image is

upgraded by a log transformation as in 8 & is shown in fig. 6

Fig. 6. Spectrum of the image block

3.4.3. In Fig. 7 extent of frequencies in Bandpass

filter is shown. The cut-off frequency values for lower & higher limit are

taken as 130 and 225 respectively for sharpness. Multiply the FFT spectrum of

the block by the filter H (u,v)

G = H ? F

3.4.4.Counter FFT transform is used to recover the

filtered image.

3.4.5.On repeating the above mentioned process the

enhanced image is obtained after the initial calculations are applied as shown

in Fig. 8

Fig. 7. Image

obtained after using Gaussian Bandpass Filter

Fig. 8.

Magnified image after using Gaussian Bandpass Filter

4.

RESULT:

4.1. FOR

STFT , PYRAMID BASED FILTERING &

CURVED GABOR FILTERING

In the given Table 1 the outcome of validity

of techniques used for fingerprint image magnification are given, based on the

EER’s percentage from the review papers mentioned above.

TABLE 1. RESULTS OF

FINGERPRINT IMAGE ENHANCEMENT TECHNIQUES

Author

Techniques

Result/Accuracy

Bhowmik,etal. 1

DFT and Histogram Equilization

Quality of fingerprint image greatly increased and

more accurate minutiae extraction

Chikkerur,

Cartwright,

Govindaraju 2

Short Time Fourier Transform (STFT) Analysis

% of EER (FVC2004)

DB1 =

19.1

DB2 =

11.9

DBE =

7.6

DB4 =

10.9

Table 1 (Continued)

Fronthaler

Kollreider, Bigun 5

Pyramid- based filtering

% of EER (FVC2004)

DB1 =

12.0

DB2 =

8.2

DBE =

7.6

DB4 =

7.0

Gottschlich 7

Curved Gabour Filters

% of EER (FVC2004)

DB1 =

9.7

DB2 =

6.3

DBE =

5.1

DB4 =

6.5

EER = Equal Error

Rate, FAR = False Acceptance Rate, FRR = False Rejection Rate

(FROM THIS TABLE

REMOVE THE FOLLOWING HEADINGS AS I HAVE NOT INCLUDED THEM IN THIS REVIEW PAPER

– 1,5,7)

EER

value given in the above table shows that the segments of both FAR & FRR

are equal. The lower the EER value, higher the authenticity of the biometric

system. The functioning of curved Gabor filters 6 depends on the nature &

authenticity of the orientation fields (OF) and RF approximation.

4.2. RESULT

FOR FAST FOURIER TRANSFORM & GAUSSIAN BANDPASS FILTERS WILL CONCLUDE THE

ANSWER

Assessments

were conducted on as many as 138 Fingerprint images gathered from 46 different

people from all class & categories using an Optical Fingerprint Reader. The

size of each image is 480 x 320. Highpass and Bandpass frequency domain filters

were used to affirm the best technique used in the process of fingerprint

improvement.

The

following guidelines & framework are investigate the methodology discussed

above.

4.2.1. PSNR: Peak

Signal-to-noise ratio

The

Peak Signal to Noise Ratio (PSNR) produce a degree

of

peak error in decibels between two images. This ratio is often used as a

quality measurement between the original image and a compressed or

reconstructed image 9. The technique with a higher PSNR produce better

results. PSNR is expressed as:

PSNR = 10log10

Here

R is the ultimate desirable pixel value in the input image. The value of R is

taken as 255 for an 8-bit unspecified total data type gray scale image. The MSE

produces the aggregate squared error between the recreated & the pioneer

image. Here, the lower value of MSE shows lower error in the reformation of an

image. The mean-squared error is given as:

= I(I , j) – K ( I , j)2

where,

M & N are the number of rows and columns in the suggested images

subsequently. Image I is to be investigated & image K will work as the

relating image.

4.2.2. Calculation

Time:

This measure is used to guage the

total time taken to magnify the image & can be ascertained by using cputime

command of Matlab.

Table-II

shows the conclusions drawn empirically by implementing frequency domain

filters on the sample Fingerprint images using Matlab.

TABLE II

EXPERIMENTAL

RESULTS

Enhancement Technique

Peak Signal to Noise ratio

Computation Time

Gaussian High pass Filter

7.4657db

0.8892 secs

Gaussian High pass Filter

26.4865db

0.8644 secs

5.

CONCLUSION:

The realization of a

fingerprint feature derivation & coordination calculations largely rely on

the quality of fingerprint images used for desired results. There have been

constant efforts made in enhancing the quality of fingerprint images as shown

in different studies. The oriented diffusion filter & curved Gabor filter

process give preferred results.

In the latter

half of the paper the other methods & techniques used for fingerprint

enhancement such as, Gaussian Bandpass filter is used in the frequency domain.

Here, with the use of PSNR the filtered image is capable of reducing the noise

& smoothening the holes & small breaks in a ridge in less time frame

required for calculation. The Fourier spectrum can be rotated undeviatingly

with the developed Bandpass Filter tuned to a limited range of cut off frequencies.

This work has concentrated on the circularly proportionate filters that are

stated as functions of the distance from the center of the filters. The

futuristic opportunity of this task can be to investigate more frequency domain

filters to accomplish improved outcomes.

6.

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