Frequency Domain Enhancement Algorithm Based On

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 - Min utiae 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

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© 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.

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 made a literature survey before implementing the steps for an Automatic Identification and Authentication enhancement tasks. Image enhancement can be done Systems. Minutiae are the local fingerprint patterns mostly in in the spatial or frequency domain. Anil K. Jain et al., the form of terminations and bifurcations. True minutiae are used Gabor filter for frequency domain of ridges; and needed for further process. Those true minutiae are extracted used band pass filter to capture negative frequency only from a good quality and better enhanced image. To 2012 achieve this, we propose a frequency domain enhancement response too as intensity values to change abruptly from white to black at the pores. They applied wavelet pril algorithm based on the Log-Gabor Filtering Technique on the A

transforms which is a high localized property in both

Fast Fourier‟s Frequency domain. The performance of the

algorithm is measured in terms of Peak Signal to Noise Ratio frequency and spatial domains. Hence they used 15 and Mean Square Error and Standard Deviations. maxican hat wavelet transforms [1, 2]. Slobodan Ribaric Keywords : Bifurcation, FFT, Frequency-domain, Log- et al. [3] followed histogram fitting for normalization. Gabor, Termination. L.Hong[5] proposed the filtering technique to enhance the fingerprint image. K.Kanagalakshmi et al. [7, 8, and I. INTRODUCTION 9] proposed a filtering technique based on Median filter. he fingerprint recognition is being widely applied in It is a good filtering technique according to performance the personal identification for the purpose of high and takes less computational time; it removes salt and T degree of security. However, some acquired pepper noises in the spatial domain. Low-pass, Band- fingerprint images are poor in quality which corrupts the pass, and Butterworth Fitters are used for the image accuracy of fingerprint recognition consequently. smoothing [10]. A complex Gabor filter can be defined Fingerprint image enhancement is usually an initial step as the product of a Gaussian Kernel times a complex in most of the Automatic Fingerprint Identification (AFIS), sinusoid [11]. Jianwie et. al. [12] designed a new and Automatic Fingerprint Authentication System method MGF (Modified Gabor Filter) to overcome the (AFAS). problem of TGF (Traditional Gabor Filter). MGF follows In our everyday life from personal access an image-independent parameter selection scheme. control to border control, fingerprint identification The Gabor function have been recognized a very applications are playing a vital role. The need of powerful tool in the areas of computer vision, image fingerprint image enhancement is unavoidable for poor processing and pattern recognition. It is particularly quality images where revocable region contain used for texture analysis due to its optimal localization necessary features for matching. To implement this properties in both spatial and frequency domain.. The objective, a new algorithm based on Log-Gabor with Log-Gabor filter has a response of Gaussian when FFT is proposed. The rest of the paper is structured as viewed on logarithmic frequency scale instead of a follows: Section 2 describes the background study and linear one. It lets more information to be captured in high experiment made before the proposal of algorithm. In frequency areas and also it has desirable high pass section 3, the proposed algorithm is explained. The characteristics [13, 14, and 15]. Biometric based Experimental results are discussed in section 4; and authentication and Identification system [16] needs an section 5 concludes the work. accurate and enhanced image. Eun-Kyung Yun et al. [17] proposed an adaptive fingerprint image II. BACKGROUND WORK enhancement technique to extract different features. Fingerprint have been used as a popular Carsten Gottschlich [18] implemented Curved Gabor Filters for the Fingerprint image enhancements. It biometric for the automated identification and Global Journal of Computer Science and Technology Volume XII Issue VII Version I authentication purpose due to the high acceptability, stands-in an important role in the enhancement of universality, and uniqueness. Accuracy of identification fingerprint images. Sang Keun Oh et al. [19] proposed a and authentication are based on the quality images. We method based on directional filer bank to regularize the structure of the ridge patterns of fingerprint image. Author α : Doctoral Research Scholar, DJ Academy for Managerial Sapasian M. et al. [20] used a technique of contrast Excellence, Coimbatore, Tamilnadu, India. limited adaptive histogram equalization associated with E-mail : [email protected] clip limit standard deviation and sliding neighborhood Author σ : Director, SNS Rajalakshmi College of Arts and Science, Coimbatore, Tamilnadu, India. E-mail : [email protected] stages to enhance fingerprint image. Chaohong Wu et

al. [21] used a filtering technique called Directional Fast Median Filter for the better enhancement. Chengpu Yu Fourier Transform et al. [22] designed an enhancement method with the (FFT) 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 Design Enhanced Log-Gabor Image [24]; the log Gabor‟s frequency response is symmetric Filter (LGF) 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 Frequency Domain 2012 high frequency end. Wei Wang et al. [25] proposed Log- Enhancement Gabor filtering method. Gabor filtering is the most trendy pril

A fingerprint enhancement method. To overcome the

limitations of traditional Gabor filer and to promote the

16 fingerprint performance, the log- Gabor filter is developed which promotes the quality and reliability of Inverse Transform fingerprint identification. Chunfeng Hu et.al [26] FFT Image 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 Fig 1: Flow chart of the proposed fingerprint of all categories of regions: well-defined region, enhancement algorithm recoverable corrupted region and unrecoverable a) Algorithm corrupted region of fingerprint images. The proposed The flow chart of the proposed algorithm is algorithm includes different steps which are given in shown in fig.1. The main steps of the algorithm next. 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 Global Journal of Computer Science and Technology Volume XII Issue VII Version I 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

Fourier Transformation on the image in order to enhance component of Log-Gabor function [24] can be the frequency domain using eqns. 1and 2. The Fourier expressed: Transform produces a complex number valued output image which can be displayed with two images, either (3) with the real and imaginary part or with magnitude and phase. More often, only the magnitude of the FFT is where r is the normalized radius from centre, rfo displayed in image processing due to the reason of the is the normalized radius from centre of frequency plane magnitude which contains the most of the information of corresponding to the wavelength. the geometric structure of the spatial domain image 2. The angular component: It controls the orientation [10]. that the filter responds to.

(1) (4)

(2) Where FC is the angular filter component; it is 2012

obtained by calculating angular distance d of sin and pril A

Where ωN is an Nth root of unity. cosine. The Log-Gabor filtering technique (eqn. 5) is

Step 2 Log-Gabor Filter Design (LGF): derived from eqns. 3 and 4.

17 Fig. 2 shows the flowchart of the Log-Gabor Filter design. The LGF needs some filter components to (5) design a filter. The filters are constructed in terms of two components [15]: Step3 : Frequency Domain Enhancement

The scope of this paper is focused on block- 1. The Radial component. based contextual filtering. It is classified into spatial and 2. The Angular component. frequency domain. Our focal point is on frequency

The following parameters are also required to domain enhancement. The Log-Gabor Filter is applied support two different components in order to design on the Fast Fourier-Transformed frequency domain Log-Gabor filter: image to get an enhanced image (TI). Eqn.1 and 5 details the way to enhance the frequency domain using  The minimum and maximum frequencies wish to FFT and LGF respectively (see eqn. 6). cover.  The filter bandwidth to use. (6)  The scaling between centre frequencies of successive filters. Step 4 : Inverse Transform of FFT  The number of filter scales. The Fourier domain image has much greater

 The number of filter orientations to use. range than the image in the spatial domain. Hence, its

 The angular spread of each filter. values are generally calculated and they are stored in

float values. To retransform the Fourier image into the

Construct Radial correct spatial domain after Log-Gabor filtering in the Component frequency domain, both the magnitude and the phase of the Fourier Image must be preserved. Mean while shifting also performed before the reverse

Construct Angular transformation to transform the output of the FFT by Component 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 Product of Radial and spectrum. After that the retransformation is Angular Component accomplished by using the eqn. 7.

(7) Global Journal of Computer Science and Technology Volume XII Issue VII Version I Log-Gabor Filter Step 5 : Enhanced Output Finally, the reverse transformation of the Fourier Fig. 2 : Flow chart of the Log-Gabor filter Design Image results an enhanced image with smoothened

 Construction of Filter ridge structures as shown in the fig. 3. In addition to the algorithmic steps, we followed thinning and 1. Constructi ng radial component: It controls the morphological operations. frequency band that the filter responds to. Radial

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) Fingerpri No. of No. of Total nt Image # Terminations Bifurcations Minutia

(a) 101_1.tif 138 5 143

2012 102_1.tif 317 5 322 103_1.tif 61 106 167 pril

104_1.tif 241 24 265

18 105_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 (b) Fp9.tif 180 30 210

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 (c) Fingerpri No. of No. of Total Fig 3 : Output of algorithm flow (a) Original Image (FVC nt Image # Terminations Bifurcations Minutia 2001 DB: 101_1.tif) (b) Fast Fourier Transformed Image 101_1.tif 40 56 96 (c) Result of Log-Gabor Filtering on FFT image 102_1.tif 38 41 79 (enhanced) 103_1.tif 94 16 110 104_1.tif 97 43 140 105_1.tif 81 59 140 IV. RESULTS AND DISCUSSIONS Fp1.tif 73 59 132 The proposed algorithm is implemented on the Fp3.tif 62 105 167 Fp5.tif 69 159 228 fingerprints from FVC 2001 Database and also on the Fp7.tif 48 114 162 real time fingerprint. It tested over the well-defined, Fp9.tif 59 136 195

recoverable corrupted and unrecoverable corrupted b) Performance measure Global Journal of Computer Science and Technology Volume XII Issue VII Version I structure of fingerprint images. After implementing the algorithm again tried to extract minutiae from the Our work includes the evaluation of proposed

enhanced fingerprint image. frequency-domain enhancement algorithm based on the following quality and noise measures. MSE ( Mean a) Exper imental Results Square Error) Experiment I: Minutia Extraction before Enhancement Before implementing the proposed algorithm, (8) minutiae (Terminations and Bifurcations) of the

Where R is the maximum fluctuation in the input c) Discussions image and MSE is the Mean Square Error; I is an input The results are evidence for the variation image an FI is the filtered image; M and N are the rows between the minutia extracted before and after the and columns of the input image. enhancement. Table I and II clearly furnish the number of Terminations and Bifurcations extracted before and  PSNR ( Peak Signal to Noise Ratio) after enhancement. From experiment I and II it is confirmed that the images before enhancement has (9) more furs (blurring of edges) in the ridge and more isolated minutiae which leads the increased terminations Mean Square Error and the Peak Signal to and decreased bifurcations respectively. After the Noise Ratio and Standard Deviation are calculated using enhancement, the fingerprint features are smoothened. equations (8) and (9) and are furnished in the Table III. It returns the decreased terminations and increased Table III : Quality Measures Of Proposed Algorithm bifurcations due to the reduced furs and removed break (101_1.Tif) point; and also clear features are derived from the 2012 proposed method. The experimental results are shown pril

MSE PSNR STANDARD A

in the appendix for human perception (pls. Refer

DEVIATION Appendix).

19 15.451 36.2410 71.1126 V. CONCLUSION A Frequency domain enhancement algorithm  PSD (Power Spectral Density) and Gaussian Distribution based on Log-Gabor and FFT was proposed and implemented. From the implementation results, it is The FFT power spectral density of Log-Gabor is found that the maximum variations between original and found with respect to frequency for 200Hz and charted enhanced images; and also the increased number of in fig. 4. In addition to that the Gaussian charge terminations and decreased number of bifurcations due distribution also plotted in fig. 5. to the un-smoothing and noisiness. The results proved that the proposed algorithm can be a better one for the frequency domain enhancement.

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APPENDIX (a) Before Enhancement (b) After Enhancement

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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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