Performance Enhancement of Minutiae Extraction Using Frequency

International Journal of Pure and Applied Mathematics Volume 118 No. 7 2018, 647-654 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue

ijpam.eu

Performance Enhancement of Minutiae Extraction Using Frequency and Spatial Domain Filters R.Anandha Jothi

Dr.V.Palanisamy Professor & Head

Research Scholar Department of Computer Applications Alagappa University Karaikudi. TamilNadu, India. 1 [email protected]

Department of Computer Applications Alagappa University Karaikudi. TamilNadu, India. [email protected] 2

Abstract— Minutiae based feature extraction method is an important system for person identification. Yet spurious and false minutiae are often occurring. The fingerprint images are hardly in good quality. They may be corrupted due to deviation in skin condition (dry, wet) and skin impression. Therefore, fingerprint image enrichment techniques are working prior to minutiae extraction to accomplish more reliable estimation of minutiae positions. So to overcome these issues, in this proposed work, spatial and frequency domain filters are effectively implemented in fingerprint image pre-processing processes individually and followed by minutiae are extracted. Subsequently fingerprint image quality is verified in terms of MSE, PSNR and SSIM and found to be good for AMF. Average value of performance evaluation in minutiae extraction is found be 0.31and 0.10 for FFT and AMF respectively. Hence FFT can be used effectively in fingerprint minutiae extraction in person authentication. Keywords— Fingerprint; minutiae extraction; Fast Fourier Transform; spatial domain filter

I.

INTRODUCTION

Most of the person identification systems used biometric characteristics such as behavioral and physical traits of a person. This supports identification of individual among groups of others. The fingerprint is one of the most reliable biometric features. As it remains unique for a specific individual and has also been verified to be more perfect. Therefore, fingerprint helps to be the maximum acceptable, widespread and matured biometric characteristics. A fingerprint is concerned with pattern of interleaved ridges and valleys. The group of local characteristic and their relationship are determining the individuality and invariant features of the fingerprint. The most often used fingerprint features are minutiae, Minutiae has two significant characteristics such as ridge ending and ridge bifurcation that organize a fingerprint pattern. The fingerprint feature extraction process carried out by enhancement or pre-processing, minutiae detection and

647

extraction. Therefore, minutiae based fingerprint authentication method offers reasonable identification speed and accuracy. Also, minutiae based feature extraction method is well acknowledged around the crime and forensic investigation organizations of maximum countries. The minutiae map contains around 70 to 100 minutiae points and matching accuracy is reduced while the size of the database is growing up. Hence, it is unavoidable to make the size of the fingerprint feature code to be lesser than possible; finally the identification may be considerably faster and easier.Automatic fingerprint identification system (AFIS) for minutiae based technique usually consists of the following phases (i) Image pre-processing (ii) Feature detection and extraction. In this work a comparative study was conducted on the Performance Evaluation (PE) of extracted minutiae from the two approaches such as frequency and spatial filters. The rest of the paper is planned as follows: Section II describes Frequency domain filter, i.e. fast Fourier Transformation (FFT) method for fingerprint enhancement. Section III presents the methods of spatial nonlinear filters (Min/Max, midpoint, Adaptive Median Filters (AMF)) for fingerprint enhancement. Section IV The minutiae extraction process were deliberated. Section V gives experimental results and discussions are reported. Section V provides conclusion of this paper. II. RELATED WORK Fingerprint image enhancement or pre-processing techniques are studied by various researchers for removing noise and other ambiguous effects, Chaohong Wu [1] et.al. Proposed directional median filters to eliminate noise and spurious signals for fingerprint image enhancement. Shlomo Greenberg et al. [2] studied two different methods for removing noise and improve the quality of fingerprint images. The first method carried out by wiener filtering for binarization and thinned image. The second method uses an anisotropic filter for direct grayscale enhancement. Both methods were improving the

International Journal of Pure and Applied Mathematics

minutiae detection process. Choudhart et al. [3] studied frequency and spatial filtering for local orientation; frequency estimation and morphological approach are utilized to improve the clarity and structure of the ridge structure of the given input image. B.G.Sherlock et.al. [4, 5] Studied two type filters for fingerprint image pre-processing. The non-stationary directional Fourier filter for image enhancement and Directional filters for image smoothing. The experimental results of this technique expressively better for AFIS. E.Chandar et al. [6] tested the median filter on both binary and gray scale image to make better image quality and the performance is calculates by statistical correlation and computational time. This is easiest process but unable to remove the maximum of noise. III. ENHANCEMENT METHODS Fingerprint enhancement is a technique of improving the quality of an image by increasing brightness, contrast and sharpening of the minutiae, furthermore to eliminate noise and other unwanted discontinuities. Accordingly enhancement process is a major role in pre-processing. Enhanced image would become try to preserve true minutiae as possible. Simultaneously destroying the ambiguous patterns such as spurs break and noise digital image enhancement methods categorize two types (i) Frequency domain filtering method (ii) Spatial domain filtering methods. The process of frequency domain techniques based on modifying Fourier transform of image [7]. The spatial domain methods referred to image plane and based on direct manipulation of pixels [6, 8, and 9].The proposed framework as given below fig.1.

Special Issue

Additionally the enhanced image was subjected to FFT filtering, the filtered image were using easier recognition of ridges and key features [11,12]. The FFT based pre-processed image was partitioned into overlapping tiny blocks. Normally the square block size was taken 2 k where k is a whole number. The power of 2 is taken so that fast radix-2 FFT can be used and thus optimized the speed. The FFT of the block was computed by the equation (1,2 and 3). 𝑀−1

𝑁−1

𝑥=0

𝑦=0

𝐹 𝑢, 𝑣 =

𝑓 𝑥, 𝑦 ∗ 𝑒𝑥𝑝 −𝑗2𝜋

𝑢𝑥 𝑣𝑦 + … (1 ) 𝑀 𝑁 To enhance a particular block by its dominant frequencies, multiply FFT of the block by its magnitude a set of times as. ×

𝑔 𝑥, 𝑦 = 𝐹 −1 𝐹 𝑢, 𝑣 × 𝐹 𝑢, 𝑣

𝑘

…… (2)

Where 𝐹 −1 (𝑓(𝑢, 𝑣)) is given as: 𝑓 𝑥, 𝑦 =

1 𝑀𝑁

𝑀−1

𝑁−1

𝑢=0

𝑣=0

𝐹 𝑢, 𝑣 𝑢𝑥 𝑣𝑦 + 𝑀 𝑁 𝑓𝑜𝑟 𝑥, 𝑦 = 0,1,2 … .31 ∗ 𝑒𝑥𝑝 𝑗2𝜋 ×

… . (3)

Where K is constant, a higher value of ―k‖ can increase the presence of the ridges by filling up tiny holes in ridges, however highest value of ―k‖ may result in spurious ridges causes an endpoint to become a bifurcation or respectively. An FFT based fingerprint enhancement or pre-processing is shown in fig.2.

(a)

(b)

Fig. 1 Schematic Diagram of pre-processing A. Fast Fourier Transform (FFT) for enhancement Fast Fourier transform is a technique where the given input image is transformed from spatial to frequency domain. The FFT based enhancement algorithm includes of the following subsequent steps (i) Normalization (ii) Segmentation (iii) Orientation image estimation (iv) 2D Fourier transform (v) Inverse 2D Fourier transform and reconstruction. The original image was subjected to contrast enhancement achieved by Adaptive Histogram Equalization (AHE). This helps in further essential process and found better minutiae extraction [10].

648

(c)

(d)

Fig. 2 (a) AHE image (b) FFT image (c) Binarized image

International Journal of Pure and Applied Mathematics

Special Issue

𝑍𝑚𝑎𝑥 = 𝑆𝑥𝑦 is the maximum\ highest grey level value. 𝑍𝑥𝑦 = (𝑥, 𝑦) grey level at the subject matter

(d) Extracted Minutiae image

coordinates.

𝑍𝑚𝑒𝑑 = is the maximum possible 𝑆𝑥𝑦 window size.

B. Spatial Filtering Further Spatial domain filtering based enhancement method was used for pre-processing, the outcome of filtering produce faithful minutiae [13]. Most of the filters are used for image noise removal. Which is used for different task i.e. noise reduction, interpolation, re-sampling and blurring. Blurring is used to remove small dots and unwanted structures from an image earlier to large object extraction. The selection of filter depends upon the type and amount of noise presented in an image subsequently those dissimilar filters can remove different types of noise effectively. The spatial domain filters has two kinds, such as linear and Non-linear filters, in this work we have achieved by Non-linear Min/Max, Midpoint filter and Adaptive Median filters (AMF).Non-linear filters exhibit well performance than linear filters [14].The nonlinear filters functionality is based on the ranking of pixels enclosed in the image area covered by the filter, and then replaces the value of middle pixel with the value determined by the ranking result. The type of spatial domain filters as follows. C. Min and Max Filter The smallest and the largest value within the pixel values are selected by the minimum and maximum filter. In this process, the maximum and minimum intensity values are found within a window of certain pixels. The output remains unchanged when the central pixel lies within the intensity range of its neighbors. If the central pixel is greater than the maximum value within that window, then it is set to the maximum value and if it is less than the minimum value than it takes the minimum value to itself [15]. The min/max filter can be represented by the following equation (4) and (5) 𝑓 𝑥, 𝑦 = max {𝑔 𝑠, 𝑡 } … (4) (𝑠,𝑡)∈𝑆𝑥𝑦

𝑓 𝑥, 𝑦 = min {𝑔 𝑠, 𝑡 } … (5)

Algorithm We should analyze the following definition of AMF at two levels. Let us the function A and B. Level A: A1 =𝑍𝑚𝑒𝑑 − 𝑍min A2 =𝑍𝑚𝑒𝑑 − 𝑍max If A1 > 0 and A2 < 0 then do to definition B. Level B: B1=𝑍𝑥𝑦 B2=𝑍𝑥𝑦

− 𝑍min − 𝑍max

If B1 > 0 and B2 < 0, output 𝑍𝑥𝑦 Else output

.

𝑍𝑚𝑒𝑑 .

From the above level A and B of AMF effectively to eliminate the impulse noise .The definition of Level A and B are given below. Explanation of level A: To determine whether the output value of the filter 𝑍𝑚𝑒𝑑 is in the noise or not If 𝑍min < 𝑍𝑚𝑒𝑑 < 𝑍max the 𝑍𝑚𝑒𝑑 value is not a noise value ant it must be transmitted to the exit. Explanation of level B: To determine whether 𝑍𝑥𝑦 𝑖𝑡self is a noise level and a new value to be according to this. The definition of the above algorithm that the median value of level A is equal to the noise, in case similar this the window size to be inspected will be changed and alternative median value will be calculated. The above process will be continued up to the median value comes different between maximum or minimum value. However, this can’t guarantee that the obtained value is not noise. Nevertheless, dependent on the window size the probability of finding a noise value will be minimized. While increase the widow suppresses the noise to an excessive extent, simultaneously, proportion to its size. E. Midpoint Filter

(𝑠,𝑡)∈𝑆𝑥𝑦

D. Adaptive Median Filter (AMF) The AMF is used to determine which pixels in an image have been pretentious by impulse noise. The AMF categorizes pixels as noise and then matching each pixel in the given image to it’s around neighbor pixels. The size of the neighborhood is modifiable, In addition to the threshold for the comparison. A pixel that is dissimilar from a majority of its neighbors, in addition to not structurally align with these pixels to which it is similar, is called as impulse noise. That kind of noise pixels is replaced by the median pixel value of the pixels in the neighborhood that have passed the noise labeling test. Notation

𝑍min = 𝑆𝑥𝑦 is the minimum\ lowest grey level value .

649

The midpoint filter is used for blurring the image by replacing it with the average of the highest intensity pixel and the lowest intensity pixel within the specific window. Median filter can be signified by the below equation (6). 1 𝑓 𝑥, 𝑦 = max {𝑔 𝑠, 𝑡 + min {𝑔 𝑠, 𝑡 } … (6) (𝑠,𝑡)∈𝑆𝑥𝑦 2 (𝑠,𝑡)∈𝑆𝑥𝑦 F. Minutiae Extraction Once the frequency and spatial domain filtering has been applied for enhancing the ridge pattern, then some more steps are performed for minutiae extraction. Such as Binarization and Thinning, Binarization of the image obtained is done keeping the threshold as zero [16, 17]. A value of representation that the pixel is white and a value of zero indicate the pixel to be black. A threshold value is set for each pixel in the image. Those pixel values which are smaller than

International Journal of Pure and Applied Mathematics

the threshold is set to zero and the pixel values which are greater than the threshold is set to one. The image produced is in binary form. Thinning operation is done on the binary image obtained from the previous step. Thinned ridge lines are obtained using morphological thinning operator bwmorph [18, 19]. Finally minutiae extraction is done on the thinned image. This process is done by using a 3x3 window to examine the local neighborhood of each ridge pixel in the image. The minutiae extraction is based on crossing number concept. G. Minutiae Extraction Using Crossing Number (CN) The crossing number (CN) at a point P is defined as half of the cumulative successive differences [19, 20] and is expressed as follows in equation (7). 1 CN = 2

B. Structural Similarity Index Measure (SSIM) The structural similarity index method is based on measurement of three components such as luminance, structure and contrast comparison. SSIM process is combined with these separate components. The structural similarity amongst the input image x and enhanced image y [23, 24, 25]. SSIM was calculated by the equation (9).

|Pi − Pi+1 |

(7)

𝑖=1

Property Isolated Point Ridge Ending Point Continuing Ridge Point Bifurcation Point Crossing Point

Where 𝜇𝑥 , 𝜇𝑦 are means and𝜎𝑥2 , 𝜎𝑦2 are variances of x and y respectively, covariance of x, y is 𝜎𝑥 , 𝜎𝑦 and 𝐶1 , 𝐶2 are adaptable constants, and L is the utmost possible value of x. The input fingerprint images from FVC2004 DB3 database was taken for measuring the SSIM index between original and enhanced images. C. Mean Squared Error (MSE) MSE is calculated by an average squared intensity of the original and resultant image pixels [26]. There was computed by the equation (10). Here e (m, n) is error difference between the original and the distorted images.

The end points and branching points or minutiae are detected using the above mentioned properties of CN. The skeleton image is scanned for detecting the minutiae. It has been found that for ―valid‖ bifurcation points an additional condition of a number of neighbors equal to three is required. VI.

After preprocessing the fingerprint image quality was measured by various quality measurement techniques. Those techniques are used to estimate the enhancement effect. The most universally accepted objective measures are i) Structural Similarity Index Measure (SSIM) ii) Peak signal to noise ratio (PSNR) and iii) Mean Squared Error (MSE).

8

Where Pi is the value in the neighborhood of P. Pi= (0,1) and I has a period of 8, that is P9=P1.For any pixel P, we consider the 8 neighboring pixels in a 3x3 neighborhood, each of which can take a value of either 1 or 0 as follows: CN has the following properties. CN 0 1 2 3 4

Special Issue

1 𝑀𝑆𝐸 = 𝑁𝑀

𝑀−1 𝑁−1

𝑒(𝑚, 𝑛) 2

(10)

𝑚 =0 𝑛=0

D. Peak Signal to Noise Ratio (PSNR)

PERFORMANCE EVALUATION (PE)

To evaluate the performance of enhancement filters, the perceived minutiae are compared with a set of minutiae attained from the same fingerprint by a human expert. The performance evaluation (PE) is expressed by the following equation (8).

𝑃𝐸 =

𝑀𝑝 −𝑀𝑚 −𝑀𝑠 𝑇𝑜𝑡𝑎𝑙 𝐻

(8)

Wherever Mp is the number of pairs matched within the enhanced image, Mm and Ms Represent the number of dropped, spurious minutiae respectively, and TotalH is denoted as the number of minutiae extracted by the human experts. The maximum value of PE=1, All the total number of minutiae are correctly paired with the corresponding detected minutiae and there are no missing minutiae (Mp=TotalH and Mm=Ms=0) respectively [21, 22]. Hence all the detected minutiae are matched or paired. A large value of PE for a fingerprint image denotes that the enhancement filters have done a good performance on the image. A. Quality Measure

650

SNR is a mathematical measure of image quality based on the pixel difference between input and resultant images. The SNR to measure the quality of the reconstructed image compared with the original image. Where S=255 for an 8-bit pixel image. The PSNR is fundamentally the SNR while all pixel values are equivalent to the maximum possible value [27]. PSNR value was calculated by the given equation (11). 𝑃𝑆𝑁𝑅 = 10𝑙𝑜𝑔 VII.

𝑆2 𝑀𝑆𝐸

(11)

RESULT AND DISCUSSION

A. Quality Metrics To measure the quality of frequency and spatial filter based on enhanced fingerprint image. Yet there are many techniques to evaluate the enhanced fingerprint image quality such as MSE, PSNR, SSIM that indices depict in table 1 to 4 .The comparison of filters explained by charts shown in fig 3,4 and 5 respectively. The AMF based PSNR and SSIM values are improved and the value of MSE reduced to compare with

International Journal of Pure and Applied Mathematics

other filtering method. AMF based enhanced PSNR, SSIM indices attains the maximum possible values against other filtering methods. This clearly indicates that the AMF method enhances gives a better quality fingerprint image as compared to other filters.

Table 1. Quality measures for min\max filters. Finger print image Min\Max Filters

Special Issue

Table 4. Quality measures for FFT Finger print image

FFT

DB3

MSE

PSNR

SSIM

DB3_B_102_7.tif

128.327

25.3571

0.9728

DB3_B_103_5.tif

115.321

25.5786

0.9835

DB3_B_104_7.tif

121.604

26.6036

0.9615

DB3_B_105_2.tif

160.596

24.5242

0.9677

DB2

MSE

PSNR

SSIM

DB3_B_106_1.tif

135.448

24.8351

0.9782

DB3_B_102_7.tif

198.329

22.3152

0.8365

DB3_B_107_3.tif

112.574

25.5623

0.9736

DB3_B_103_5.tif

117.386

24.4153

0.8591

DB3_B_108_5.tif

134.553

25.1203

0.9685

DB3_B_104_7.tif

97.567

26.4695

0.8771

DB3_B_109_7.tif

130.788

25.4128

0.9745

DB3_B_105_2.tif

196.562

22.5421

0.8382

DB3_B_110_8.tif

157.752

24.5531

0.9352

DB3_B_106_1.tif

190.505

22.0283

0.8241

DB3_B_107_3.tif

225.300

23.1250

0.8525

B. Performance Evaluation

DB3_B_108_5.tif

250.568

20.4862

0.8291

DB3_B_109_7.tif

230.021

21.3549

0.8328

DB3_B_110_8.tif

210.185

20.3110

0.8152

To compare the performance of both filters based on extracted minutiae. The input image was selected from a standard fingerprint database FVC2004 of DB2 & DB3. Table 5 & 6 depict the maximum and minimum value of PE of FFT and AMF for this data set are 0.43- 0.11 and 0.21- 0.03, similarly with an average value of PE is 0.31 to 0.10 respectively. The PE value calculated more than 100 fingerprint images. Hence, Table 5 & 6 shows only 6 value those values are randomly chosen based on the number of spurious minutiae. Such minutiae are grouped by average (between 13 to 15), below average (between 11 to 12) and above average (between 16 to 17). The statistical comparison of those filters, FFT based PE value is much better than AMF based PE value.

DB3_B_110_9.tif 197.574 21.3841 0.8388 Table 2. Quality measures adaptive median filters. Finger print image Adaptive Median Filter DB2

MSE

PSNR

SSIM

DB3_B_102_7.tif

98.7945

37.1524

0.9897

DB3_B_103_5.tif

94.8033

40.4026

0.9952

DB3_B_104_7.tif

83.1192

40.3852

0.9912

DB3_B_105_2.tif

88.3536

37.2702

0.9857

DB3_B_106_1.tif

100.2391

36.1271

0.9891

DB3_B_107_3.tif

110.3678

35.2491

0.9754

DB3_B_108_5.tif

96.9082

35.0842

0.9711

DB3_B_109_7.tif

90.2107

36.2341

0.9823

DB3_B_110_8.tif

89.1081

33.1782

0.9701

DB3_B_110_9.tif

101.4592

35.0247

0.9714

Table 5. PE value for FVC2004 DB2& DB3 database of five fingerprint images for FFT filtering based extracted minutiae. Fingerprint image T

Table 3. Quality measures for Midpoint Finger print image

Midpoint

DB3

MSE

PSNR

SSIM

DB3_B_102_7.tif

220.235

21.2215

0.8168

DB3_B_103_5.tif

190.544

21.4356

0.7582

DB3_B_104_7.tif

160.236

22.3548

0.8971

DB3_B_105_2.tif

223.225

19.6544

0.878

DB3_B_106_1.tif

230.754

19.2756

0.802

DB3_B_107_3.tif

252.184

21.4599

0.8576

DB3_B_108_5.tif

263.698

18.415

0.8025

DB3_B_109_7.tif

245.541

17.3542

0.7897

DB3_B_110_8.tif

253.145

19.3633

0.7928

651

DB2_B_101_1.tif DB2_B_106_3.tif DB3_B_110_1.tif DB3_B_101_3.tif DB2_B_109_7.tif DB3_B_105_2.tif Average PE value

Mp 30 33 26 32 23 37

28 28 25 29 22 35

d 2 4 7 3 4 2

M e 1 3 2 2 3 3

Ms

PE

17 11 15 12 13 17

0.30 0.39 0.11 0.43 0.21 0.43 0.31

Table 6. PE value for FVC2004 DB2 & DB3 database of five fingerprint images for adaptive median filtering (AMF) based extracted minutiae. Fingerprint image

T

Mp

DB2_B_101_1.tif DB2_B_106_3.tif DB3_B_110_1.tif

30 33 26

25 26 22

d 5 6 4

M e 1 3 2

Ms

PE

18 13 16

0.06 0.21 0.07

International Journal of Pure and Applied Mathematics

DB2_B_110_1.tif DB3_B_101_3.tif DB3_B_105_2.tif Average PE value

26 32 37

21 25 31

3 7 6

3 2 3

17 15 19

Special Issue

0.03 0.09 0.16 0.10

Fig. 5 SSIM value for Spatial and frequency domain filters

VIII.

CONCLUSION

Fingerprint image enhancement has been successfully studied using frequency and spatial domain filtering techniques. Herein, AHE, Filtering, Binarization, Thinning and minutiae extraction applied effectively on individual fingerprint and implemented in MATLAB. The quality of the reconstructed images is determined by measuring the MSE, PSNR, and SSIM and performance is evaluated using DB3, DB2 of FVC2004 database. The proposed fingerprint enhancement system using spatial filtering techniques gives high PSNR, SSIM when compared to the frequency filtering. However, frequency domain filter based PE value is higher when compared to the spatial filtering method.

Fig. 3 MSE value for Spatial and frequency domain filters

References [1]

Chaohong Wu, Sergey Tulyakov and Venu Govindaraju, Image Enhancement Method using Directional Median Filter, in Proc. SPIE conf. on Biometric Technology for Human Identification. [2] Greenberg-―Fingerprint Image Enhancement Using Filtering Techniques‖-Pattern Recognition, 2000, proceedings 15th International Conference, Volume 3, page(s)322-325 . [3] J.Choudhary, Dr.S.Sharma, J.S.Verma, ―A new framework for improving low quality fingerprint images,‖ international journal of computer technology and application. Vol.2, no.6, pp.1859 1866,2011. [4] Schneider, J.K., Glenn,W.E., 1996. Surface Feature Mapping Using High Resolution C-Span Ultrasonography. US Patent 5587533. [5] Sherlock, D., Monro, D.M., Millard, K., 1994. Fingerprint enhancement by directional Fourier filtering. In: IEEE Proc. on Visual Imaging Signal Processing, vol. 141, pp. 87–94. [6] E.Chandra et.al Noise Suppression Scheme using Median Filter in Gray and Binary Images, International Journal of Computer Applications (0975 -8887) Volume 26–No.1, July 2011 [7] Rafael C. Gonzalez, Rechard E. Woods, Digital Image Procesing, Pearson,Third Edition,2008. [8] E.Chandra and K.Kanagalakshmi, Noise Elimination in Fingerprint Images using Median Filter, International Journal of Advanced Networking and Applications,(2011),Vol 02, Issue:06, pp:950-955. [9] 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.: CFP1195FPRT, IEEE Explore. [10] Youlian Zhu, Cheng Huang, An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping. Gray Level Mapping ―, 2012 International Conference on Solid State Devices and Materials Science, Physics Procedia 25 (2012) 601 – 608 1875-3892 © 2012 Published by Elsevier. Pp. 601 – 608. [11] Neethu S, Sreelakshmi S, Deepa Sankar ―Enhancement of fingerprint using FFT×|FFT|n filter‖ International Conference on

Fig. 4 PSNR value for Spatial and frequency domain filters

652

International Journal of Pure and Applied Mathematics

[12]

[13]

[14]

[15] [16]

[17]

[18]

[19]

[20]

[21]

[22]

[23] [24] [25] [26]

[27]

Information and Communication Technologies (ICICT 2014) 2015 The Authors. Published by Elsevier , ( 2015 ) 1561 – 1568 Abdullah Toprak a, ˙Inan Guler , Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter, Digital signal processing, Digital Signal Processing 17 (2007) 711–723. Divya.V-―Adaptive Fingerprint Image Enhancement Based On Spatial Contextual Filtering And Preprocessing Of Data‖-IJCAT journal Volume1,Issue 4,2014 K. Somasundaram and P. Kalavathi, ―Medical Image Denoising using Non-Linear Spatial Mean Filters for Edge Detection.‖, rural.univ.ac.in, pp. 149-153 R.C.Gonzalez and R.E.Woods, ―Digital Image Processing,‖ second ed., Prentice Hall, Englewood, Englewood, Cliffs, NJ, 2002. Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition. 28(11), 1657–1672 (1995). Zhixin Shi, Venu Govindaraju: A chaincode based scheme for fingerprint feature extraction.Pattern Recognition Letters. 27, 462– 468 (2006). Tico, M., Kuosmanen, P.: An algorithm for fingerprint image postprocessing. In: Proc. of the Thirty-Fourth Asilomar Conference on Signals Systems and Computers, pp. 1735-1739 (2000). Zaho, F., Tang, X.: Preprocessing and post-processing for skeleton-based fingerprint minutiae Extraction. Pattern Recognition. 40(4), 1270–1281 (2007). Ching-Tang Hsieh, Eugene Lai, You-Chuang Wang, An effective algorithm for fingerprint image enhancement based on wavelet transforms Pattern Recognition 36 (2003) 303 – 312 D. Peralta, M. Galar, I. Triguero, O. Miguel-Hurtado, J.M. Benitez, F. Herrera, Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms, Elsevier, Engineering Applications of Artificial Intelligence 32(2014) 37-53. Alan C. Brooks, Xiaonan Zhao, Thrasyvoulos N. Pappas, Structural similarity quality metrics in a codingcontext: exploring the space of realistic distortions, IEEE Transactions on image processing, vol. 17, no. 8, Aug. 2008 1 D. Maltoni, D. Maio, A.K. Jain and S. Prabhakar, Handbook of Fingerprint Recognition, Springer, New York, 2009 Z. Wang and A. C. Bovik, ―A universal image quality index,‖ IEEE Signal Process. Lett., vol. 9, no. 3, pp. 81– 84, Mar. 2002. Z. Wang and A. C. Bovik, Modern Image Quality Assessment. San Rafael, CA: Morgan and Claypool, 2006. WU Xiao-Feng, HU Shi-gang, ZHAO Jin, LI Zhi-ming, LI Jin, TANG Zhi-jun, XI Zai-fang, Comparative analysis of different methods for image enhancement, © Central South University Press and Springer-Verlag Berlin Heidelberg 2014 , J. Cent. South Univ. (2014) 21: 4563−4570, DOI: 10.1007/s11771-014-2461-y L. M. Jean-Bernard Martens, "Image dissimilarity," Signal Processing, vol. 70, no. 3, pp. 155-176, 1998.

653

Special Issue

654