Frequency Domain Enhancement Algorithm Based on Log …...Gabor filtering method. Gabor filtering is the most trendy fingerprint enhancement method. To overcome the limitations of

© 2012. Mrs.K.Kanagalakshmi & Dr.E.Chandra. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.

Global Journal of Computer Science and Technology Volume 12 Issue 7 Version 1.0 April 2012 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350

Frequency Domain Enhancement Algorithm Based on Log -Gabor

Filter in FFT Domain

By Mrs.K.Kanagalakshmi & Dr.E.Chandra

DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, India

Abstract - Minutiae extraction is one of the most important

steps for an Automatic Identification and Authentication

Systems. Minutiae are the local fingerprint patterns mostly in

the form of terminations and bifurcations. True minutiae are needed for further process. Those true minutiae are extracted

only from a good quality and better enhanced image. To

achieve this, we propose a frequency domain enhancement

algorithm based on the Log-Gabor Filtering Technique on the

Fast Fourier‟s Frequency domain. The performance of the

algorithm is measured in terms of Peak Signal to Noise Ratio

and Mean Square Error and Standard Deviations.

Keywords : Bifurcation, FFT, Frequency-domain, Log-Gabor, Termination.

GJCST Classification: i.5.m

Frequency Domain Enhancement Algorithm Based On Log-Gabor Filter in FFT Domain

Strictly as per the compliance and regulations of:

Frequency Domain Enhancement Algorithm Based on Log-Gabor Filter in FFT Domain

Mrs.K.Kanagalakshmi α & Dr.E.Chandra σ

Abstract - Minutiae extraction is one of the most important steps for an Automatic Identification and Authentication Systems. Minutiae are the local fingerprint patterns mostly in the form of terminations and bifurcations. True minutiae are needed for further process. Those true minutiae are extracted only from a good quality and better enhanced image. To achieve this, we propose a frequency domain enhancement algorithm based on the Log-Gabor Filtering Technique on the Fast Fourier‟s Frequency domain. The performance of the algorithm is measured in terms of Peak Signal to Noise Ratio and Mean Square Error and Standard Deviations. Keywords : Bifurcation, FFT, Frequency-domain, Log-Gabor, Termination.

I. INTRODUCTION

he fingerprint recognition is being widely applied in the personal identification for the purpose of high degree of security. However, some acquired

fingerprint images are poor in quality which corrupts the accuracy of fingerprint recognition consequently. Fingerprint image enhancement is usually an initial step in most of the Automatic Fingerprint Identification (AFIS), and Automatic Fingerprint Authentication System (AFAS).

In our everyday life from personal access control to border control, fingerprint identification applications are playing a vital role. The need of fingerprint image enhancement is unavoidable for poor quality images where revocable region contain necessary features for matching. To implement this objective, a new algorithm based on Log-Gabor with FFT is proposed. The rest of the paper is structured as follows: Section 2 describes the background study and experiment made before the proposal of algorithm. In section 3, the proposed algorithm is explained. The Experimental results are discussed in section 4; and section 5 concludes the work.

II. BACKGROUND WORK

Fingerprint have been used as a popular biometric for the automated identification and authentication purpose due to the high acceptability, universality, and uniqueness. Accuracy of identification and authentication are based on the quality images. We Author α : Doctoral Research Scholar, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, India. E-mail : [email protected] Author σ : Director, SNS Rajalakshmi College of Arts and Science, Coimbatore, Tamilnadu, India. E-mail : [email protected]

made a literature survey before implementing the enhancement tasks. Image enhancement can be done in the spatial or frequency domain. Anil K. Jain et al., used Gabor filter for frequency domain of ridges; and used band pass filter to capture negative frequency response too as intensity values to change abruptly from white to black at the pores. They applied wavelet transforms which is a high localized property in both frequency and spatial domains. Hence they used maxican hat wavelet transforms [1, 2]. Slobodan Ribaric et al. [3] followed histogram fitting for normalization. L.Hong[5] proposed the filtering technique to enhance the fingerprint image. K.Kanagalakshmi et al. [7, 8, and 9] proposed a filtering technique based on Median filter. It is a good filtering technique according to performance and takes less computational time; it removes salt and pepper noises in the spatial domain. Low-pass, Band-pass, and Butterworth Fitters are used for the image smoothing [10]. A complex Gabor filter can be defined as the product of a Gaussian Kernel times a complex sinusoid [11]. Jianwie et. al. [12] designed a new method MGF (Modified Gabor Filter) to overcome the problem of TGF (Traditional Gabor Filter). MGF follows an image-independent parameter selection scheme. The Gabor function have been recognized a very powerful tool in the areas of computer vision, image processing and pattern recognition. It is particularly used for texture analysis due to its optimal localization properties in both spatial and frequency domain.. The Log-Gabor filter has a response of Gaussian when viewed on logarithmic frequency scale instead of a linear one. It lets more information to be captured in high frequency areas and also it has desirable high pass characteristics [13, 14, and 15]. Biometric based authentication and Identification system [16] needs an accurate and enhanced image. Eun-Kyung Yun et al. [17] proposed an adaptive fingerprint image enhancement technique to extract different features. Carsten Gottschlich [18] implemented Curved Gabor Filters for the Fingerprint image enhancements. It stands-in an important role in the enhancement of fingerprint images. Sang Keun Oh et al. [19] proposed a method based on directional filer bank to regularize the structure of the ridge patterns of fingerprint image. Sapasian M. et al. [20] used a technique of contrast limited adaptive histogram equalization associated with clip limit standard deviation and sliding neighborhood stages to enhance fingerprint image. Chaohong Wu et

T

© 2012 Global Journals Inc. (US)

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al. [21] used a filtering technique called Directional Median Filter for the better enhancement. Chengpu Yu et al. [22] designed an enhancement method with the association of Gabor, Diffusion, Low-pass and band-pass filters in different dimension of features. The Gabor Filters are widely used for enhancement purpose [23]. The Log-Gabor function is proposed by David. J. Field [24]; the log Gabor‟s frequency response is symmetric on a log axis. One of the advantages of Log Gabor is its use with codes in which the bandwidths increase with frequency i.e. they are constant in octave. The two important characteristics of log-Gabor function are; it has no DC component and it has an extended tail at the high frequency end. Wei Wang et al. [25] proposed Log-Gabor filtering method. Gabor filtering is the most trendy fingerprint enhancement method. To overcome the limitations of traditional Gabor filer and to promote the fingerprint performance, the log- Gabor filter is developed which promotes the quality and reliability of fingerprint identification. Chunfeng Hu et.al [26] developed a fingerprint segmentation technique in association with the Log-Gabor filtering and orientation reliability. The Log-Gabor filter makes the non-ridge areas dark and ridge areas brighter in fingerprint image.

We reviewed and analyzed practically the different spatial and frequency domain filters; and performed evaluation on selective frequency domain enhancement filters such as Low-Pass Filter, Band- Pass Filter, Butterworth Filter and Log-Gabor Filter. The experiment results show that the Log-Gabor filter can provide a better performance and also better image smoothing than Low Pass Filter, Band Pass filter, and Butterworth Filter [27].

III. PROPOSED ALGORITHM

Our proposed algorithm aims at the enhancement of all categories of regions: well-defined region, recoverable corrupted region and unrecoverable corrupted region of fingerprint images. The proposed algorithm includes different steps which are given in next.

Fig 1: Flow chart of the proposed fingerprint enhancement algorithm

a) Algorithm The flow chart of the proposed algorithm is

shown in fig.1. The main steps of the algorithm compress:

1. FFT: Compute the Fast Fourier Transform of the acquired image I. IF =FFT (I)

2. LGF Design: Design a Log-Gabor Filter (LGF). 3. Frequency Domain Enhancement: Apply LGF on

Fourier Transformed image. TI = IF *LGF

4. Inverse Transform of FFT: Perform inverse transform of the image which is derived from step 3. I `= (TI)-1

5. Output of Enhanced Image: Obtain the processed and Log-Gabor enhanced image.

Step 1: Image acquisition is the preliminary step followed while implementing the proposed algorithm. The first step of the algorithm is performing the Fast

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12Frequency Domain Enhancement Algorithm Based On Log-Gabor Filter in FFT Domain

Enhanced

Image

Fast

Fourier

Transform

(FFT)

Design

Log-Gabor

Filter (LGF)

Frequency Domain Enhancement

Inverse

Transform

FFT Image

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Fourier Transformation on the image in order to enhance the frequency domain using eqns. 1and 2. The Fourier Transform produces a complex number valued output image which can be displayed with two images, either with the real and imaginary part or with magnitude and phase. More often, only the magnitude of the FFT is displayed in image processing due to the reason of the magnitude which contains the most of the information of the geometric structure of the spatial domain image [10].

(1)

(2)

Where ωN is an Nth root of unity. Step 2 Log-Gabor Filter Design (LGF):

Fig. 2 shows the flowchart of the Log-Gabor Filter design. The LGF needs some filter components to design a filter. The filters are constructed in terms of two components [15]:

1. The Radial component. 2. The Angular component.

The following parameters are also required to support two different components in order to design Log-Gabor filter:

The minimum and maximum frequencies wish to cover.

The filter bandwidth to use. The scaling between centre frequencies of

successive filters. The number of filter scales. The number of filter orientations to use. The angular spread of each filter.

Fig.

2 :

Flow chart of the Log-Gabor filter Design

Construction of Filter

1.

Constructing radial component: It controls the frequency band that the filter responds to. Radial

component of Log-Gabor function [24] can be expressed:

(3)

where r is the normalized radius from centre, rfo is the normalized radius from centre of frequency plane corresponding to the wavelength.

2.

The angular component: It controls the orientation that the filter responds to.

(4)

Where FC is the angular filter component; it is obtained by calculating angular distance d of sin and cosine. The Log-Gabor filtering technique (eqn. 5) is derived from eqns. 3 and 4.

(5)

Step3

: Frequency Domain Enhancement

The scope of this paper is focused on block-based contextual filtering. It is classified into spatial and frequency domain. Our focal point is on frequency domain enhancement. The Log-Gabor Filter is applied on the Fast Fourier-Transformed frequency domain image to get an enhanced image (TI). Eqn.1 and 5 details the way to enhance the frequency domain using FFT and LGF respectively (see eqn. 6).

(6)

Step 4

: Inverse Transform of FFT

The

Fourier domain image has much greater range than the image in the spatial domain. Hence, its values are generally calculated and they are stored in float values. To retransform the Fourier image into the correct spatial domain after Log-Gabor filtering in

the frequency domain, both the magnitude and the phase of the Fourier Image must be preserved. Mean while shifting also performed before the reverse transformation to transform the output of the FFT by moving the zero frequency component into the centre of the array. It is very useful to visualize a Fourier transform with the zero-frequency component in the middle of the spectrum. After that the retransformation is accomplished by using the eqn. 7.

(7)

Step 5 : Enhanced Output

Finally, the reverse transformation of the Fourier Image results an enhanced image with smoothened


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Construct Radial

Component

Construct Angular

Component

Product of Radial and

Angular Component

Log-Gabor Filter

ridge structures as shown in the fig. 3. In addition to the algorithmic steps, we followed thinning and morphological operations.

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(a)

(b)

(c)

Fig

3 :

Output of algorithm flow (a) Original Image (FVC 2001 DB: 101_1.tif) (b) Fast Fourier Transformed Image

(c) Result of Log-Gabor Filtering on FFT image (enhanced)

IV.

RESULTS AND DISCUSSIONS

The proposed algorithm is implemented on the fingerprints from FVC 2001 Database and also on the real time fingerprint. It tested over the well-defined, recoverable corrupted and unrecoverable corrupted structure of fingerprint images. After implementing the algorithm again tried to extract minutiae from the enhanced fingerprint image.

a)

Experimental Results

Experiment I: Minutia Extraction before Enhancement

Before implementing the proposed algorithm, minutiae (Terminations and Bifurcations) of the

fingerprints were extracted. It underwent the tasks of acquiring original image, binarization, and thinning and minutia extraction. That is the direct minutiae extraction was performed without filtering and an enhancement of an image (see Table I).

Table I

:

Minutiae Extraction Before Enhancement (Fvc 2001 Db2 (101_#.Tif) And Real-Time Ngerprints

(Fp#.Tif)

Experiment II: Minutia Extraction after Enhancement

During the second experiment the new proposed algorithm is implemented in order to get frequency domain enhanced image. As per the algorithms procedure described in section 3, experiment was followed and obtained the results of minutia. That is the minutiae are extracted from the Log-Gabor and FFT enhanced image. Table II shows the number of terminations and bifurcations extracted.

Table II

:

Minutiae Extraction After Enhancement

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Fingerpri

nt Image #

No. of

Terminations

No. of

Bifurcations

Total

Minutia

101_1.tif 138 5 143

102_1.tif 317 5 322

103_1.tif 61 106 167

104_1.tif 241 24 265

105_1.tif 231 13 244

Fp1.tif 236 17 253

Fp3.tif 116 51 167

Fp5.tif 204 49 253

Fp7.tif 152 27 179

Fp9.tif 180 30 210

Fingerpri

nt Image #

No. of

Terminations

No. of

Bifurcations

Total

Minutia

101_1.tif 40 56 96102_1.tif 38 41 79103_1.tif 94 16 110104_1.tif 97 43 140105_1.tif 81 59 140

Fp1.tif 73 59 132Fp3.tif 62 105 167Fp5.tif 69 159 228Fp7.tif 48 114 162Fp9.tif 59 136 195

b) Performance measure Our work includes the evaluation of proposed

frequency-domain enhancement algorithm based on the following quality and noise measures. MSE ( Mean Square Error)

(8)

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Where R is

the maximum fluctuation in the input image and MSE is the Mean Square Error; I is an input image an FI is the filtered image; M and N are the rows and columns of the input image.

PSNR ( Peak Signal to Noise Ratio)

(9)

Mean Square Error and the Peak Signal to Noise Ratio and Standard Deviation are calculated using equations (8) and (9) and are furnished in the Table III.

Table III

:

Quality Measures Of Proposed Algorithm (101_1.Tif)

PSD (Power Spectral Density) and Gaussian Distribution

The FFT power spectral density of Log-Gabor is found with respect to frequency for 200Hz and charted in fig. 4. In addition to that the Gaussian charge distribution also plotted in fig. 5.

Fig

4 :

The Fast Fourier Transformations power spectral density of Log-Gabor Filter

Fig

5 :

Gaussian distribution of an image

c)

Discussions

The results are evidence for the variation between the minutia extracted before and after the enhancement. Table I and II clearly furnish the number of Terminations and Bifurcations extracted before and after enhancement. From experiment I and II it is confirmed that the images before enhancement has more furs (blurring of edges) in the ridge and more isolated minutiae which leads the increased terminations

and decreased bifurcations respectively. After the enhancement, the fingerprint features are smoothened. It returns the decreased terminations and increased bifurcations due to the reduced furs and removed break point; and also clear features are derived from the proposed method. The experimental results are shown in the appendix for human perception (pls. Refer Appendix).

V.

CONCLUSION

A Frequency domain enhancement algorithm based on Log-Gabor and FFT was proposed and implemented. From the implementation results, it is found that the maximum variations between original and enhanced images; and also the increased number of


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MSE PSNR STANDARD

DEVIATION

15.451 36.2410 71.1126

010

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4050

0

20

40

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5

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terminations and decreased number of bifurcations due to the un-smoothing and noisiness. The results proved that the proposed algorithm can be a better one for the frequency domain enhancement.

REFERENCES RÉFÉRENCES REFERENCIAS

1. C.Burru, R.Gopinath, and H.Guo, Introduction to Wavelet Transforms, Prentice Hall, 1998.

2. Anil K. Jain, Yi Chen, and Meltem Demirkus, Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 1, January 2007.

3. Slobodan Ribaric and Ivan Fratric, Biometric Identification System Based on Eigen palm and Eigen finger Features, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 11, November 2005.

4. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, Image Quality Assessment : from error visibility to structural similarity, IEEE Transactions Image Processing, Vol. 13, No. 4, pp-600-612, Apr. 2004.

5. L.Hong, Y.Wan, and A.K.Jain, Fingerprint Image enhancement algorithms and Performanc Evaluation, IEEE Transactions Analysis and Machine Intelligence,1998, Vol. 20, no.8, pp-777-789.

6. Raju Sonavane , Dr. B.S. Sawant, Noisy Fingerprint Image Enhancement Technique for Image Analysis: A Structure Similarity Measure Approach, Int. Journal of Computer Science amd Network Security, 2007, Vol.7 No.9.pp225-230.

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7.

E.Chandra and K.Kanagalakshmi, Noise Elimination in Fingerprint Images using Median Filter, Int. Journal of Advanced Networking and Applications,(2011),Vol. 02, Issue:06, pp:950-955.

8.

K.Kanagalakshmi and E.Chandra, Performance Evaluation of Filters in Noise Removal of Fingerprint Image, Proceedings of ICECT-2011, 3rd

International Conference on Electronics and Computer Technology, April 8-10 2011, pp vol.1: 117-123, ISBN: 978-1-4244-8677-9, Published by IEEE, Catalog no.: CFP1195F-PRT, IEEE Xplore.

9.

E.Chandra and K.Kanagalakshmi, Noise Suppression Scheme using Median Filer in Gray and Binary Images, International Journal of Computer Applications (0975 –

8887) Volume 26–

No.1, pp. 49-57 , July 2011.

10.

Rafael C. Gonzalez, Rechard E. Woods, Digital Image Procesing, Pearson,Third Edition,2008.

11.

Javier R. Movellan, Tutorial of Gabor Filters.

12.

Jianwei Yang, Lifeng Liu, Tianzi Jiang, and Yong Fan, A Modified Gabor Design method for fingerprint image enhancement, Pattern Recognition Letters, Elsevier, 24, 1805 –

1817,2003.

13.

Jamie Cook, Vinod Chandran, Sridharan and Clinton Fookes, Goabor Filter Bank Representation for 3D Face Recognition, Proceedings of the Digital Image Computing Techniques and Applications (DICTA 2005), Published by IEEE,2005, doi: 0-7695-2467-2.

14.

C Liu and H. Wechsler, Independent Component Analysis of Gabor Features for Face Recognition, IEEE Transactions. Neural Networks, vol. 14, no. 4, pp. 919–928, 2003.

15.

Peter Kovesi, Invariant Measures of Image Features from Phase Information, Thesis.

16.

E.Chandra and K.Kanagalakshmi, Cancelable Biometric Templae Generation

of Protection Schemes: a Review, Proceedings of ICNCS -2011, International Confernece on Network and Computer Science, IEEE Xplore.

17.

Eun-Kyung Yun, Sung-Bae Cho, Adaptive fingerprint image enhancement with fingerprint image quality analysis, Image and Vision Computing, Elsevier, 24 (2006) 101-110.

18.

Carsten Gottschlich, Curved Gabor Filters for Fingerprint Image Enhancement, arXiv:1104.4298v1[cs.CV] 21 Apr 2011.

19.

Sang Keun Oh, Joon Jae Lee, Chul Hyun Park, Bum Soo Kim, Kil Houm Park, New Fingerprint Image Enhancement Using Directional Filter Bank, Journal of WSCG, Vol.11, No.1., ISSN 1213-6972, WSCG‟2003, February 3-7, 2003.

20.

M. Sepasian, W. Balachandran and C. Mares, Image Enhancement for Fingerprint Minutiae-Based Algorithms Using CLAHE, Standard Deviation

Analysis and Sliding Neighborhood, Proceedings of

the World Congress on Engineering and Computer Science 2008, WCECS 2008, October 22 -

24, 2008.

21.

Chaohong Wu, Sergey Tulyakov and Venu Govindaraju, Image Enhancement Method using Directional Median Filter,

in Proc. SPIE conf. on Biometric Technology for Human Identification.

22.

Chengpu Yu, Mei Xie, and Jin Qi, An Effective and Robust Fingerprint Enhancement Method, Proc. ISCID 08 Int. National Symposium on Computational Intelligence and Design, vol. 01, IEEE Computer Society, 2008.

23.

Teddy Ko, Fingerprint Enhancement by Spectral Analysis Techniques, Proc. of the 31st Applied Image Pattern Recognition Workshop, ACM digital Library, 2002.

24.

David J.Field, “ Relations between the statistics of natural images and the response properties of Cortical Cells”, Journal of Optical Society of America, Vol. 4, No. 12,pp. 2379-2394, Dec. 1987.

25.

Wei Wang, Jianwei Li, Feifei Huang, Hailiang Feng, Design and implementation of Log-Gabor filter in fingerprint image enhancement, Pattern Recognition Letters, Vol. 29, Issue 3 Pages 301-308, Feb 2008.

26.

Chunfeng Hu, Jianping Yin, En Zhu, Hui Chen, Yong Li ,A composite fingerprint segmentation based on Log-Gabor filter and orientation reliability , IEEE

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International Conference on Image Processing(ICIP), pp,3097-3100, 2010.

27. Dr.E.Chandra and K.Kanagalakshmi, Frequency Domain Enhancement Filters for Fingerprint Images:

A Performance Evaluation, CIIT International Journal of Digital Image Processing, Vol.3, No. 16, Oct. 2011.

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APPENDIX

(a)

Before

Enhancement

(b) After Enhancement

Fig

6 :

Minutiae Extraction (a) Before Enhancement (b) After Enhancement (FVC: 101_1.tif) (Red-

Termination and Green –

Bifurcation)

(a)

Before Enhancement (b) After Enhancement

Fig

7 :

Minutiae Extraction (a) Before Enhancement (b) After Enhancement

(Real time fingerprint: fp5.tif)


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Documents

Frequency Domain Enhancement Algorithm Based on Log …...Gabor filtering method. Gabor filtering is the most trendy fingerprint enhancement method. To overcome the limitations of