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1 Efficiency Analysis of Compared Normalization Methods for Fingerprint Image Enhancement 1 Marko Kočevar, 2 Zdravko Kačič 1 Margento R&D d.o.o., Gosposvetska cesta 84, 2000 Maribor, Slovenia 2 Faculty of Electrical Engineering and Computer Science, Smetanova 17, 2000 Maribor, Slovenia ABSTRACT The efficiency of automated fingerprint identification system depends highly on fingerprint image enhancement algorithms. Fingerprint image enhancement can be divided into two stages: 1) At the first stage we enhance the image with conventional filters, where filter parameters do not change. 2) At the second stage we enhance the structure of ridges and valleys with contextual filters, where filter parameters change according to pre-calculated ridge orientation and frequency. This article presents an efficiency analysis of compared methods at the first stage with global, local and block local, attempts to find the most efficient combination and evaluates the effectiveness of analysed methods from the perspective of their use in real time fingerprint identification systems. In the experimental part we compared three different types of algorithms for enhancing image contrast, and then in combination with the second enhancement stage (Gabor filter and STFT) we assessed the enhancement system's effectiveness on database FVC2004. Keywords: fingerprint enhancement, fingerprint recognition system, image 1 INTRODUCTION Fingerprint is a physiological characteristic widely used in biometrics. Biometrics is a science dealing in recognition of persons' behavioural (speech, writing, gait, etc.) and physiological characteristics (fingerprint, iris pattern, face, palm, etc.). Personal identification on the basis of fingerprints belongs to the field of biometric systems and fingerprints are acknowledged as the most precise biometric characteristics. However the accuracy of the automated fingerprint identification system highly depends on the quality of fingerprints, because feature extraction algorithms may incorrectly extract the features, needed for fingerprint matching. Fingerprints may be dirty, damp, too dry, damaged, etc. Therefore it is necessary to enhance the fingerprint image to the point where we get a clear structure of ridges and valleys on the fingerprint's surface (Figure 1). Most often and most efficiently used features, on which fingerprint matching is based, are called minutiae. The most useful are two types of minutiae (Figure 1): minutiae point where a ridge ends, and minutiae point where a ridge divides into two ridges. With conventional filters, such as [2], histogram equalization [1], Wiener filtering [4], etc. we enhance the fingerprint image's contrast, which is very important for further image processing. This article presents an efficiency analysis of compared methods with global, local and block local, attempts to find the most efficient combination and evaluates the effectiveness of analysed methods from the perspective of their use in real time fingerprint identification systems. Figure 1. Ridges and valleys and minutiae points 2 IMAGE ENHANCEMENT PROCEDURE In this section we will present enhancement of fingerprint image's contrast with three algorithms and enhancement of ridge structure with contextual filters, such as Gabor filter [2] and STFT [3]. Fingerprint image enhancement with contextual filters can be done in the spatial or in the frequency domain. 2.1 Normalization Fingerprint image is a procedure with which we determine uniform grey value in a fingerprint image. We thus decrease the variation of grey level values around ridges and valleys. The result of is an equally distributed grey value in a fingerprint image Global In [2] the authors presented fingerprint image's contrast enhancement with global, which is performed uniformly on an entire image. Normalization involves the following two steps: 40

Step 1: determining the mean value and the variance for an entire fingerprint image according to the following equation: = 1 0 0 1( (, ) )2, (, ) (1) different fingerprint areas, which is why local

2 Step 1: determining the mean value and the variance for an entire fingerprint image according to the following equation: = ( (, ) )2, (, ) (1) different fingerprint areas, which is why local is more appropriate. The image is first divided into blocks, and is determined for each pixel (, ) in a block with the following equation: norimg (i, j) = + (I (, ) ) (4) = (, ), (, ) (2) Step 2: image with the following equation: = (5) where and are the default mean value and variance. According to empirical data [5] = 128 and = (, ) = ( (, ) ) ( (, ) ) (, ) > and are the default mean value and variance, which are usually determined experimentally. In this case = 0 and = Local Unlike with global, the average of pixels in a local area and not the entire image is considered with local. A fingerprint image is of different quality in (3) Block local Similarly as with local, which is determined for each pixel individually, block local [7] is calculated for each block of the fingerprint image. We previously divided a greyscale image into non-overlapping blocks BLKSIZE of size = 4 with the centre in window WNDNORM of size = 8, where windows partially overlap (Figure 3). Normalization is determined for an entire block according to the default mean value ( M = 128) and variance (V = ) of the window WNDNORM. Figure 2. a) presentation of block BLKSIZE with the size W = 4 4, where we calculated the local of window WNDNORM; b) presentation of an analysis of movement of blocks and corresponding windows 3 CONTEXTUAL FILTERING Most often used technique for fingerprint image enhancement is based on contextual filters and was introduced by O'Gorman and Nickerson [6]. With contextual filtering, the characteristics of the filter change according to the local context. Usually the filter sets are pre-calculated and then chosen for a particular image area. On a fingerprint image the context is often defined by the local ridge frequency and orientation. On the basis of contextual filters many fingerprint image enhancement techniques have been developed, both in the spatial domain (contextual filtering [6], Gabor filters [2, 14, 15], curved Gabor filter [9], anisotropic filters [4], directional filters [8, 9, 10], compensation filters [11, 12]), and in the frequency domain (Log Gabor filter [16], wavelet transform [17], fast Fourier transform [18, 19, 20], directional Fourier filters [19], short time transform [3], discrete cosine transform [21] and two-stage enhancement scheme for lowquality fingerprint images by learning from images [22]). 3.1 Spatial domain enhancement The most popular contextual filtering technique is presented in [2], where authors present fingerprint enhancement with Gabor filter. The enhancement procedure is based on the following steps: 1) Global : of a greyscale image for contrast enhancement (section 2.1). 2) Determining ridge orientation: in this step ridge orientation is determined, and then smoothed with Gaussian filter [2]. The image is divided into blocks of size (16 x 16). To determine the block gradient of the horizontal value 41

3 (, ) and the vertical value (, ) a gradient operator, such as Sobel mask, is used [23]. Determining the local orientation of an individual block is done with the following equation: = ( + h, + ) ( + + h, + ), (6) = ( + h, + ), (7) orientation θ. Both H and H are defined as bandpass filters, and are defined by mean value and bandwidth [1]. Filtering is performed as follows: fingerprint image I is converted into frequency domain F with FFT (Fast Fourier Transform), transfer function of filter P is in the frequency domain multiplied by F in every point, with inverse FFT image is converted back into the spatial domain. = ( + h, + ), (8) = arctan (9) 3) Determining ridge frequency: Ridge frequency is determined with x-signature, which is determined for each block of size w w (16 16) within a directed window [2]. 4) Gabor filter: Gabor filters have both orientation and frequency characteristics, as well as optimal total resolution in the spatial and frequency domains [26, 27]. Gabor filter has the shape of a sinusoidal wave (the second expression in equation 10) narrowed by the Gaussian filter (the first expression in equation 10). h(, :, ) = + (2 ), (10) = +, = +, where θ is the filter orientation, [x, y ] are new coordinates rotated in cartesian axis by angle = 90 -θ anticlockwise. (90 ) (90 ) (90 ) (90 ) In [3] authors presented fingerprint image enhancement on the basis of short time Fourier transform (STFT). Fingerprint image is divided into smaller blocks which partially overlap. Frequency band-pass filter is used on individual blocks instead of the whole image, so that it uses the information about local ridge frequency and orientation. To enhance a fingerprint image a frequency band-pass filter is used in equation (11). To implement a radial filter Butterworth band-pass is used, while angular filter H ( ) is implemented with raised cosine filter, defined in (12) in the angular domain with centre direction and angular bandwidth support. ( ) = ( ), <, 0, h, (12) By merging individual blocks we get an enhanced fingerprint image. 4 EXPERIMENTAL RESULTS Experiments were performed on database FVC2004 [22] consisting of four sub-bases. DB1_A, DB2_A, DB3_A and DB4_A. In sub-bases DB1_A and DB2_A fingerprint images were captured with an optic sensor, in sub-base DB3_A with a Thermal sweeping sensor and in the sub-base DB4_A fingerprint images were artificially generated with Synthetic Fingerprint Generator (SFinGe) [25]. For result comparison we used an indicator from Fingerprint Verification Competitions [24]. Figure 4 shows a fingerprint image normalized with different algorithms. = f is the sinusoidal wave frequency, and σ and σ are standard deviations of the Gaussian envelope along x and y axes. 3.2 Frequency domain enhancement Within frequency domain frequency band-pass filter is expressed with polar coordinates (, ), separated in the radial and angular domain and defined with the following function: (, ) = ( ) ( ) (11) where radial filter H depends on the local ridge distance = 1/, and angular filter H depends on the local ridge 42

4.1 Equal error rate evaluation Indicator EER (%) [24] was used for equal error rate evaluation: EER (Equal Error Rate) is the error rate representing the point

FMR is also known as FAR (false acceptance rates) and FNMR is known as FRR (false rejection rate).

algorithm [2] and STFT [3] at the second enhancement stage. Hong's algorithm already in the original version uses global. Block size with block local is = 4.

4 4.1 Equal error rate evaluation Indicator EER (%) [24] was used for equal error rate evaluation: EER (Equal Error Rate) is the error rate representing the point where FNMR (False Non Match Rate) and FMR (False Match Rate) are the same [1]. FMR is also known as FAR (false acceptance rates) and FNMR is known as FRR (false rejection rate). Table 1 shows equal error rate evaluation EER for fingerprint image enhancement with a combination of different procedures at the first stage of enhancement and Hong algorithm [2] and STFT [3] at the second enhancement stage. Hong's algorithm already in the original version uses global. Block size with block local is = 4. Figure 3. original fingerprint image (FVC2004 DB2_A 4_8.tif ); b) global ; c) local ; d) 4x4 block local. Figure 3. a) original fingerprint image (FVC2004 DB2_A 2_4.tif ); b) Gabor + global, c) Gabor + block local ; d) Gabor + no e) Gabor + local. Figure 4. a) STFT + no b) STFT + global, c) STFT+block local ; d) STFT + local. 43

5 Table 1: Fingerprint image enhancement on database FVC2004 and success rate indicator EER (%) results for efficient comparison of fingerprint image enhancement and total enhancement algorithm processing time (in seconds). FVC_2004 DB1_A DB2_A DB3_A DB4_A Gabor + no Gabor + global Gabor + Block local Gabor + Local STFT + no STFT + global STFT + block local STFT+ local EER % % % % Time (s) 0.84 s 0.42 s 0.56 s 0.32 s EER % % % % Time (s) 0.67 s 0.37 s 0.6 s 0.41 s EER % % 9.00 % 8.14 % Time (s) 1.75 s 0.96 s 1,21 s 0.82 s EER % 9.65 % 9.48 % 8.67 % Time (s) 15,63 s 6.57 s 8.35 s 6.48 s EER % % % 8.98 % Time (s) 1.00 s 0.40 s 0,55 s 0,44 s EER % % % 8.98 % Time (s) 0.9 s 0.40 s 0.57 s 0.32 s EER % % % % Time (s) 1.96 s 0.95 s 1.10 s 0.65 s EER 9.84 % % 9.76 % % Time (s) s 6.36 s 7.23 s 5.77 s 5 CONCLUSION This article presented a comparison of efficiency of fingerprint image's contrast enhancement in the preprocessing step with global, local and block local. To evaluate the equal error rate we combined algorithms with traditional Hong algorithm and STFT. Table 1 shows different image processing times and different equal error rate values (EER), which means that is a very important step in fingerprint image pre-processing. With Hong algorithm we found that on all four sub-bases of FVC2004 combinations with block local and local provided better results than combinations with global and no. The opposite was found with STFT enhancement, where global provided better results in sub-bases DB3_A and DB4_A than the other two algorithms. Table 1 also shows that there is almost no difference between STFT enhancement with no and STFT with global. Different fingerprint image enhancement results are obtained mostly because of images were captured with different sensors, which have an influence on the fingerprint image contrast. From the perspective of time that pre-processing takes we can see that local is slower that global or block local (even up to 15 -times slower), which is a big disadvantage in real automated fingerprint identification system where processing time is very important. The best result was obtained with a combination of Hong algorithm and block local on sub-base DB4_A. 6 REFERENCES [1] D. Maltoni, D. Maio, and A. K. Jain, Handbook of Fingerprint Recognition, Second Edition, Springer-Verlag New York, Inc., [2]L. Hong, Y. Wan, A. K. Jain, Fingerprint image Enhancement: algorithm and performance evaluation, IEEE Trans. Pattern Analalysis and Machine Intellignce, 20(8)(1998) [3]S. Chikkerur, A. Cartwright and V. Govindaraju, \Fingerprint Image Enhancement Using STFT Analysis", Pattern Recognition, vol. 40, no. 1, pp , 2007 [4] S. Greenberg, M. Aladjem, D. Kogan and I. Dimitrov, Fingerprint Image Enhancement Using Filtering Techniques", Proc. Int. Conf. on Pattern Recognition (ICPR2000), September 3-8, 2000, Barcelona, Spain, vol. 3, pp , [5] Yang J., Xiong, N., Vasilakos, A. V. Naixue, Two-Stage Enhancement Scheme for Low-Quality Fingerprint Images by Learning From the Images, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. PP, Issue: 99, pp. 1-14, 2012 [6]L.O Gorman and J. V. Nickerson, An approach to fingerprint filter design, Pattern Recog., vol. 22, no. 1, pp , 1989 [7]M. Kočevar, Z. Kačič, A. Chowdhury, B. Kotnik, Fingerprint image enhancement with two-stage algorithm and block local, 2013, in review. [8] A. M. Tahmasebi and S. Kasaei, A novel adaptive approach to fingerprint enhancement filter design, Signal Process., Image Commun., vol. 17, no. 10, pp , [9] M. Tico, V. Onnia, and P. Kuosmanen, Fingerprint image enhancement based on second directional derivative of the digital image, EURASIP J. Appl. Signal Process., vol. 2002, no. 10, p ,

6 [10] C. Gottschlich and C.-B. Sch onlieb, Oriented diffusion filtering for enhancing low-quality fingerprint images, IET Biometrics, Volume 1, issue2, June 2012, p [11] J. C. Yang, D. S. Park, and S. Yoon, Reference point determination in enhanced fingerprint image, in Proc. Int. Symp. Comput. Intell. Design, 2008, pp [12] J. C. Yang, D. S. Park, and R. Hitchcock, Effective enhancement of low-quality fingerprints with local ridge compensation, IEICE Electron. Exp., vol. 5, no. 23, pp , 2008 [13] W. Wang, J. Li, F. Huang, and H. Feng, Design and implementation of Log-Gabor filter in fingerprint image enhancement, Pattern Recog. Lett., vol. 29, no. 3, pp , [14] C. Gottschlich, Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement, Image Processing, IEEE Transactions on, vol 21, Issue: 4, pp , Apr [15] H. Fronthaler, K. Kollreider and J. Bigun, \Local Features for Enhancement and Minutiae Extraction in Fingerprints", IEEE Transactions on Image Processing, vol. 17, no. 3, pp , [16] W. Wang, J. Li, F. Huang, and H. Feng, Design and implementation of Log-Gabor filter in fingerprint image enhancement, Pattern Recog. Lett., vol. 29, no. 3, pp , [17] C. T. Hsieh, E. Lai and Y. C. Wang, \An Effective Algorithm for Fingerprint Image Enhancement Based on Wavelet Transform", Pattern Recognition, vol 36., no. 2, pp , 2003 [18] C. I. Watson, G. T. Candela, and P. J. Grother, Comparison of FFT fingerprint filtering methods for neural network classification, National Institute of Standards Technology, Gaithersburg, MD, NIST Interagency or Internal Rep. 5493, Sep [19] A. J.Willis and L.Myers, A cost-effective fingerprint recognition system for use with low-quality prints and damaged fingertips, Pattern Recog., vol. 34, no. 2, pp , [20] T. Kamei and M. Mizoguchi, Image filter design for fingerprint enhancement, in Proc. Int. Symp. Comput. Vision, Coral Gables, FL, 1995, pp [21] S. Jirachaweng and V. Areekul, Fingerprint Enhancement Based on Discrete Cosine Transform", Proc. of Int. Conf. on Biometrics (ICB2007), LNCS 4642, Springer, Berlin, Germany, pp , [22] Yang J., Xiong, N., Vasilakos, A. V. Naixue, Two-Stage Enhancement Scheme for Low-Quality Fingerprint Images by Learning From the Images, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. PP, Issue: 99, pp. 1-14, 2012 [23] Rafael C. Gonzalez, Richard E. Woods, S. Eddins, Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 2004 [24] (zadnji obisk: dne ) [25] t=&selobj=12&pathsubj=111%7c%7c12&, (zadnji obisk: dne ) [26] J. Daugman, Uncertainty Relation for Resolution in Space, Spatial-Frequency, and Orientation Optimized by Two dimensional Visual Cortical Filters, Journal Optical Society American, vol. 2, pp , [27]A.K. Jain and F. Farrokhnia, Unsupervised Texture Segmentation Using Gabor Filters, Pattern Recognition, vol. 24, no. 12, pp , ACKNOWLEDGEMENTS This operation was partly financed by the European Union, European Social Fund. This operation was implemented in the framework of the Operational Programme for Human Resources Development for the Period , Priority axis 1: Promoting entrepreneurship and adaptability, Main type of activity 1.1.: Experts and researchers for competitive enterprises. 45

Fingerprint Enhancement and Identification by Adaptive Directional Filtering

Fingerprint Enhancement and Identification by Adaptive Directional Filtering EE5359 MULTIMEDIA PROCESSING SPRING 2015 Under the guidance of Dr. K. R. Rao Presented by Vishwak R Tadisina ID:1001051048 EE5359