A Novel Adaptive Algorithm for Fingerprint Segmentation

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1 A Novel Adaptive Algorithm for Fingerprint Segmentation Sen Wang Yang Sheng Wang National Lab of Pattern Recognition Institute of Automation Chinese Academ of Sciences P.O.Bo 78 Beijing P.R.China Abstract Fingerprint image segmentation is one of the most important steps in automatic fingerprint identification and it heavil influences the performance of fingerprint identification sstem. Minutiae are local discontinuities in the fingerprint pattern hich represent ends and bifurcations. Because e ma etract false minutiae in blurred area e must classif beteen foreground and blurred area. In this paper e propose a novel segmentation algorithm based on three ne features hich are coherence contrast and main energ ratio. These three features can present the character of piels in blurred area and blank area so e have a significant improvement in fingerprint segmentation performance. We also get the result of classification from a classifier hich is a SVM (Support Vector Machine). In order to get an accurate result in classification a preprocessing is adopted to reduce the influence of noise. Through these three ne features e etract and the classifier e select a robust segmentation of fingerprint images is implemented. Keords: Fingerprint Image processing Segmentation Support vector machine 1. Introduction To implement a successful fingerprint identification sstem the first essential step is to classif hich part of fingerprint is foreground blurred area or blank area. A fingerprint is the pattern of ridges and valles on the surface of a fingertip. The uniqueness of the fingerprint can be identified b the characteristics and relationships of bifurcations and endings in ridge or valle (Figure 1). In order to compare to fingerprints a set of invariant and discriminating features are etracted from fingerprint image. Most verification sstems providing a high securit are base on minutiae matching. Figure 1: amples of minutiae. Because more poerful and intelligent image processing techniques are possible b development in computer technolog the automatic fingerprint identification sstem appears [1]. Unfortunatel noise image

2 deficienc and deformation ma make reliable minutiae detection ver difficult. Therefore one important step in automatic fingerprint identification is the segmentation of fingerprint images. The main purpose of segmentation is to determine hich part of fingerprint is foreground and hich one is blurred area and blank area. Because e ma etract false minutiae in blurred area this can heavil influences the performances of the minutiae etraction process and the performance of the overall fingerprint identification sstem. Since the segmentation can classif beteen foreground and blur area it can reduce the number of false minutiae. In man segmentation algorithms features the etract can not completel present the feature of piels so their algorithm can onl identif the blank area but can not classif beteen foreground and nois or blurred area. [3] In this paper e present an approach to preprocess the input fingerprint image (Section ). After that e use three ne features hich are coherence contrast and main energ ratio (Section 3). Then e present a classifier hich is SVM (Section 4). Finall e present some eperimental results (Section 5).. Preprocessing Because there are man noises in original fingerprint image features e etract can not present characters of piels ell. In order to reduce the influence of noises e use some steps to preprocess the input fingerprint images..1 Normalization Before e process the input fingerprint image e normalize the image to constant mean and variance. Normalization is done to remove the effects of sensor noise and finger pressure difference. I(i denote the gra value at piel (i. M and Var are the estimated mean and variance of the input fingerprint image. M 0 + N = M0 here M 0 VAR 0 variance values. ( I M) VAR0 VAR VAR0 VAR ( I M) if I > M otherise. are the desired mean and. Smooth and Histogram qualization After normalization e use Gauss-smooth to reduce the influence of noises and use histogram equalization to make the input fingerprint image look clearl. 3. Features traction The first step of segmentation is to select features. Three features are used in [3] hich are coherence the local mean and the variance. Hoever those three features can not completel present the feature of piel especiall in blurred area so e select these three ne features. 3.1 Coherence Since fingerprint mainl consists of parallel structures the coherence in foreground is higher than that in blurred area and blank area. The block is and the definition of coherence follos: i+ j+ = G ( u v) G ( u v) O u= i v= j

3 O i+ j+ ( ) = G ( u v) G ( u v) u= i v= j O i+ j+ ( ) = G ( u v) + G ( u v) u= i v= j O O + O ( i. Coherence =. G ( i Where G are the gradients at each piel. 3. Contrast One ne feature is the contrast hich is the normalization of variance. The contrast defined as: I i j m = v = ( I( i m) v Contrast =. m The v is variance in one block; the m is mean of one block. The block is and is 16. ( i I is the intensit of the image. 3.3 Main nerg Ratio The other ne feature is the main energ ratio. A good fingerprint patterns contain narro ridges separated b narro valles and this ridges follo almost parallel to each other. The Fourier spectrum of this small area reveals to high peaks ecept DC components. Hoever hile the fingerprint pattern is not good enough the peaks are not distinct. [4] (Figure ). (1) () (3) (4) Figure : (1) (3) are original images. () (4) are FFT of (1) (3) respectivel. So this method can distinguish fingerprint signal area and noise area. This feature etraction algorithm follos: 1. Divide image to blocks of sizes (16 16);. ach block applies FFT to transform image from spatial domains into frequenc domains. 3. Search the to peaks ecept DC components. The to peaks are smmetrical to the DC components. 4. Because of noise e define main energ ratio as: p1 + p Main nerg Ratio = ( + ) p1. = F( i if F( i > p 1 30% s Where F is the value of image in frequenc domains after FFT. The s is circle hich radius is r. The r is the distance from the peak to DC component and is round to the nearest integer. peak. p p1 is the value of one is the value of the other peak. p1 p is approimatel equal to. p

4 4. Design the Classifier We select man tpical blocks in both foreground and blurred area. We manuall label and compute the three features for these blocks. We get about 3000 three-dimensional patterns hich 1700 patterns are in foreground and 1300 patterns are in blurred area or blank area. With the obtained feature vectors on training sets a SVM is trained as a classifier. We adopt the radial basis function as the kernel function of SVM. When e finish training the SVM e use this SVM classifier to classif each block in an input fingerprint image into foreground and blurred or blank area. If a block is foreground then B(i =1 else B(i = 0. After image B is obtained the classification is completed. We can also compute the percentage of foreground. If the percentage of foreground regions is smaller than a threshold then the input fingerprint image is rejected. The SVM classifier is simple and less time-consuming but there also are obstacles ith the classifier. One difficult is the selection of broad and general samples. 5. periment Results We present some eperimental result of the segmentation algorithm. In our eperiments e avoided using small samples so this algorithm as tested on man different fingerprint images from standard database. We compared this segmentation algorithm ith segmentation algorithm hich onl used mean variance and segmentation algorithm hich onl use orientation. We used the SVM classifier hich as trained b 3000 patterns e selected in specific database. We also selected 500 fingerprint images hich is at random in FVC000 standard database and e select other 500 fingerprint images from the Database hich consists of 4000 fingerprint images of size TM captured b a Veridicom COMS sensor. We divided those images into blocks and got patterns from those images. We used our segmentation algorithm and other segmentation algorithms to distinguish hich block is foreground or blurred area and blank area respectivel. Our algorithm classified 4031 false blocks in foreground and 56 false blocks in background. When e used other segmentation algorithms the error rate as higher. From Table 1 the result shos that our algorithm is better than the others. We select three fingerprint images hich are in Database. One image is too dr another image has smudges and the other image is too dark and damp. Figure 3 shos results of different segmentation algorithms. The eperiment results sho that our algorithm is robust ith man kinds of input fingerprint images such as too dr images too damp images and too dark images.

Table 1: Results of different segmentation algorithm Our segmentation algorithm

false blocks in foreground number of false blocks in background error rate 4031

05% (1) () (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) Figure 3: (1) is the

hich has smudges (9) is the original fingerprint image hich is too dark and

(3) (7) (11) is the result of Segmentation use mean and variance onl.

5 Table 1: Results of different segmentation algorithm Our segmentation algorithm Segmentation use mean and variance onl Segmentation use direction onl number of false blocks in foreground number of false blocks in background error rate % % % (1) () (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) Figure 3: (1) is the original fingerprint image hich is too dr (5) is the original fingerprint image hich has smudges (9) is the original fingerprint image hich is too dark and damp () (6) (10)is the result of our algorithm. (3) (7) (11) is the result of Segmentation use mean and variance onl. (4) (8) (1) is the result of Segmentation use orientation onl. The black area is the blurred area and blank area hich are identified b different segmentation algorithms.

6 6. Conclusion and Discussion In this paper a novel segmentation algorithm for fingerprint images has been presented. Since e present three ne features our segmentation algorithm largel reduces the error of classification especiall in blurred area. The three ne features can eactl reflect the difference beteen foreground and blurred area so the make the classification more accuratel. We use the SVM classifier to classif each block of input fingerprint image into foreground and blurred area and blank area. Through eperimental result and human inspection the proposed method provides a robust and accurate segmentation result. We ill etract more efficient features in the future hich can make classification more precise. We think these ma improve the segmentation result. Reference [1] A. Jain L. Hong and R. Bolle On-Line Fingerprint Verification I Trans. Pattern Analsis and Machine Intelligence Vol. 19 no. 4 pp [] M. Hassan Ghassemian A Robust On-Line Restoration Algorithm For Fingerprint Segmentation I Trans [3] Asker M. Bazen Sabih H. Gerez Segmentation of Fingerprint Images ProRISC 001 Workshop on Circuits Sstems and Signal Processing 001 Analsis and Machine Intelligence Vol. 0 no. 8 pp [7] A Jain S Prabhakar L Hong S Pankanti Filterbank-Based Fingerprint Mathching I Tran Image Processing Vol 9 pp Ma 000 [8] Zs. M. Kovacs-Vajna R. Rovatti M. Frazzoni Fingerprint Ridge Distance Computation Methodologies Pattern Recognition Vol33 pp [9] Marius Tico Pauli Kuosmanen A Topographic Method For Fingerprint Segmentation I Trans [10] B. G. Sherlock D. M. Monro K. Millard Fingerprint nhancement B Directional Fourier Filtering I Proc. Vis Image Signal Process Vol 141 No 1994 [11]. Osuna R. Freund F. Girosi Training Support Vector Machines: an Application to Face Detection Int. Conf. on Computer Vision and Pattern Recognition [1] D. Maio D. Maltoni R. Cappelli J. L. Waman A. Jain FVC000: Fingerprint Verification Competition Biolab internal report universit of Bologna Sept [4] Toshio Kamei Masanori Mizoguchi Image Filter Design For Fingerprint nhancement I Trans [5] Weiei zhang Yangsheng Wang Singular Point Detection in Fingerprint Image Accept b ACCV 00. [6] L. Hong Y. Wan and A. Jain Fingerprint Image nhancement: Algorithm and Performance valuation I Trans. Pattern

A Robust and Real-time Multi-feature Amalgamation. Algorithm for Fingerprint Segmentation

A Robust and Real-time Multi-feature Amalgamation Algorithm for Fingerprint Segmentation Sen Wang Institute of Automation Chinese Academ of Sciences P.O.Bo 78 Beiing P.R.China100080 Yang Sheng Wang Institute