Fusion Method of Fingerprint Quality Evaluation: From the Local Gabor Feature to the Global Spatial-Frequency Structures

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1 Fusion Method of Fingerprint Quality Evaluation: From the Local abor Feature to the lobal Spatial-Frequency Structures Decong Yu, Lihong Ma,, Hanqing Lu, and Zhiqing Chen 3 D Key Lab. of Computer Networ, Dept. of Electronic Engineering, South China Univ. of Tech., uangzhou, China, yudecong@63.com, eelhma@scut.edu.cn National Lab of Pattern Recognition, Inst. Automation, Chinese Academy of Science, Beijing, China luhq@nlpr.ia.ac.cn 3 Criminal Tech. Center, Dept. of Public Security of uangdong Province, China zq_chen@63.com Abstract. We propose a new fusion method to evaluate fingerprint quality by combining both spatial and frequency features of a fingerprint image. In frequency domain, a ring structure of DFT magnitude and directional abor features are applied. In spatial domain, blac pixel ratio of central area is taen into account. These three features are the most efficient indexes for fingerprint quality assessment. Though additional features could be introduced, their slight improvement in performance will be traded off with complexity and computational load to some extent. Thus in this paper, each of the three features are first employed to assess fingerprint quality, their evaluation performance are also discussed. Then the suggested fusion approach of the three features is presented to obtain the final quality scores. We test the fusion method in our public security fingerprint database. Experimental results demonstrate that the proposed scheme can estimate the quality of fingerprint images accurately. It provides a feasible rejection of poor fingerprint images before they are presented to the fingerprint recognition system for feature extraction and matching. Introduction Fingerprint recognition system is widely used in criminal identification, ATM card verification and access control, due to its feature s individual uniqueness and age invariability. But the performance of an Automatic Fingerprint Identification System (AFIS) depends heavily on fingerprint quality which mainly concerned with sin humidity, impressing pressure, dirt, sensing mechanism, scar and other factors. A fingerprint of good quality should have clear ridge and valley patterns, and could guarantee a high performance of recognition. Therefore, an efficient criterion for fingerprint quality evaluation will be of benefit to practical applications, such as quality control of fingerprint acquisition, quality distribution analysis of fingerprint database, and threshold decision for modification of low quality images. If the fingerprint J. Blanc-Talon et al. (Eds.): ACIVS 006, LNCS 479, pp , 006. Springer-Verlag Berlin Heidelberg 006

2 Fusion Method of Fingerprint Quality Evaluation 777 quality is assessed at first, the images of poor quality could be discarded and the fingerprint acquisition repeated, the AFIS performance will finally be greatly improved. Some methods have been proposed to evaluate fingerprint quality in the past few years. Hong et al [] quantified the quality of a fingerprint image by measuring the bloc variance of gray levels, which was computed in directions orthogonal to the orientation field. The variance was then used to decide the fingerprint quality in terms of the contrast of a considered bloc. However, this method had to be carried out in a precise orientation field which may not be correctly obtained in heavy noise situation. In addition, it is computational expensive. Ratha and Bolle [] proposed another method for quality estimation in wavelet domain for Wavelet Scalar Quantization (WSQ) images. But WSQ is not a necessary step for uncompressed fingerprint images in AFIS. Shen et al [3] described a abor feature based approach to quality measurement, it also suffers from the parameter setting and excessive computation load of abor transform. Lim et al [4] employed the ratio of eigenvalues of the gradient vectors to estimate local ridge and orientation certainty, and determined the quality with the orientation flow. This can indicate the confidence of orientation estimation, provided the noise is not directional distributed. Other quality assessments include the Fourier spectrum based method [5] and the gradient oriented scheme [6], all these methods utilized only the partial information which is not sufficient to measure a fingerprint image. Hence, lobal and local information should be combined together to accomplish the evaluation tas. A hybrid method joined seven local and global features of fingerprint [7] to assess quality was reported in 005. But it is difficult to balance the quality weight of each feature, its linear weighting does not regard the nonlinear contributions of some features. In this paper, our research is focused on an accurate and feasible method for fingerprint quality measurement. A new nonlinear fusion method for quality scoring is suggested based on the combination of three efficient quality features: ring structures in frequency spectrum, abor features denoted directional information, and the blac pixel ratio of central region which reflects the contrast and the integrity of the ridge and valley patterns. Since the central region around a core point is vital to fingerprint quality evaluation, all the calculation is performed on the central region of a core with the bacground region removing. The remainder of this paper is organized as follows. Different quality features and their evaluation performance are presented in Section. The new fusion criterion and the quality scoring principle are proposed in Section 3. Experimental results are shown in Section 4 to demonstrate the validness of our method. Finally, the conclusions and discussions are given in Section 5. Features and Evaluation The fingerprint quality could be examined by many features. Three most important ones to quantity the fingerprint image quality include blac pixel ratio of central area, ring structure of frequency spectrum, and directional abor Feature. In this section, we define three quality scoring functions with corresponding respect to these three features. The quality evaluation performances of each feature are compared as well.

3 778 D. Yu et al.. Blac Pixel Ratio of Central Area ray level distribution of the region around a core point is an essential index for fingerprint quality evaluation. Fingerprint images with high contrast relate to the well separated ridges and valleys. The smearing of wet fingerprint, the disconnected ridges in dry images and the bacground pixels introduced in a fingerprint will bias the ridge-valley contrast, thus a good quality image will have high contrast between ridges and valleys, while a poor quality image has low pattern contrast, and its ridgevalley structures are usually corrupted to some extent. To quantify the ridge-valley contrast, let the blac pixel ratio be R B and the number of blac pixels be N B, w is the region length and width. R N w B B = () We suggest that a quality score Q can be calculated as follows: Q mean R 50 3 = R B 0.6 mean R B 50 3 B Th mean l mean > Th mean < Th h l Th Where ij is the intensity of pixel (i,j). mean denotes the average intensity of the central region with w =30 is the region length and width. mean = w w i= w j= ij h (). (3) For comparison, the original image and its central region are shown in Fig.. This scoring function is derived from R B statistical analysis of the three categories fingerprint images as shown in Fig.. We could observe from Fig. that a wet image with a lower R B, the better quality is. In contrast, the lower the blac pixel ratio is, and the worse a dry image will be. On the other hand, good quality fingerprint has an around constant R B and mean is valued between Th l and Th h. Th l and Th h are determined empirically. When R B is far from the constant, the fingerprint quality declines quicly. Based on the above ideas, the quality score Q is given as the afore mentioned equation (). The steps of fingerprint image quality evaluation by Q are described below. ) Locate the core point of a fingerprint image [8]. If no core point was detected, the image center is taen as a core point. ) Compute the average gray level of the 3 30 central region, whose center is located at the core point.

4 Fusion Method of Fingerprint Quality Evaluation 779 3) Classify the fingerprint images into three categories based on mean automatically: wet, good, and dry. To each category, different thresholds are used to binarize the region. 4) Compute the quality score Q using equation (). Q can detect dry and wet fingerprint images easily and the scores of good quality fingerprints are ranged in a certain extent. However, it fails to measure the quality of image whose fingerprint area is far less than pixels, because in such case the central area consists of bacground pixels whose contrast are much smaller than the foreground pixels. (a) (b) Fig.. Central region segmentation (a) the original fingerprint image ( ); (b) the central region (30 30) Fig.. The blac pixel ratio of the three categories (good, dry and wet) fingerprint image. Ring Structure in Frequency Domain Fourier transform is a useful analysis tool for distinguished global directional and frequent structures. Since ridges and valleys of fingerprint appears a distinct pattern in frequency domain, it could be applied to illustrate the fingerprint quality.

5 780 D. Yu et al. The DFT image of a good quality fingerprint image shows a ring around the origin of the frequency coordinate, because the ridge-valley patterns are quasi-periodic structures and presents a dominant frequency in most directions with an almost uniform modulus. In contrast, fingerprint images of bad quality do not appear an obvious ring in the spectrum plane, for they contain smear points, blurred edges, disconnected ridges and so on which occupy a wide range of frequency. Maing use of the above frequency characteristic of ridge-valley structures, we could modify the scoring function Q firstly defined [5]: = C 359 F = 0 Q. (4) where C is a constant normalizing the quality score in the range [0,]. F = P ( x, y ) P ( x, y ) P ( x, y ). (5) m m s s l l P x m, y ) denotes the largest ring along the angle in magnitude spectrum, ( m ( s, s ) m m and x y = ( x, y ), and y ( x, y ) 3 x l, l ) = m are the reference points ( m along the same direction. In this paper, we detect the maximum spectrum ring first to retain an accurate quality score before the calculation of equation (4). This modification is based on the fact that the variation of ridge-valley distance in different fingerprints will result in a negative F if we compute equation (5) directly. The detection is performed by selecting the i th ring with the following equation: i 359 = i, Max{ P ( x, y)}, 30 i 40. (6) i = 0 where P ( x, y) denotes the frequency band, and ( x, y) is its coordinate. The integer i is the radius of the maximum spectrum ring. The P ( x, y) band located at 30 to 40 pixels from the origin along the angle, because the central area of the fingerprint image is region of interest, its average ridge and valley distance is around 9 pixels, thus the corresponding ring of high spectrum magnitudes will appear in the frequency band located at 30 to 40 pixels away from the origin in the spectrum image. Our algorithm can be briefly stated as follows: ) Locate the core point of a fingerprint image. ) Perform FFT on the central region. 3) Find the largest ring in frequency plane using equation (6). 4) Compute the score Q with regard to the largest spectrum ring using eq. (4). Since Fourier transform is calculated on the whole central region, Q is a global estimate to fingerprint quality. Even if the fingerprint is partially bad, its quality score

6 Fusion Method of Fingerprint Quality Evaluation 78 remains low. This global frequency feature taes on advantage of accurate assessment of the whole region, but it lacs of the characteristic of local pattern measurement..3 Directional abor Features abor filter has both orientation and frequency selective properties, Fingerprint images of good quality have a strong orientation tendency and a well-defined spatial frequency. For blocs of good quality, abor feature of one direction or several angles are lager than those in others direction; while for bad quality blocs, the abor features become close to each directions. Hence the standard deviation of abor features of different orientations can be used to judge the fingerprint quality. The general form of a D abor filter is defined by x y h x, y,, f0 ) = exp{ ( + )}cos(πf x ) =,,m. (7) σ σ ( 0 x y x y = xcos + ysin. (8) = x sin + y cos. (9) th where is the orientation of the filter ban h( x, y,, f0), f 0 is the frequency of a sinusoidal plane wave, m denotes the number of orientations, and σ x and σ y are the standard deviation of the aussian envelope along the x and y axes, respectively. The magnitude abor feature at each w w bloc centered at (X,Y) can be defined as: w/ w/ g( X, Y,, f ) = I ( X + x, Y + y) h( x, y,, f ). (0) 0 x= w/ y= w/ where I(x,y) denotes the intensity of the pixel (x,y), w is the size of a bloc. = π ( ) / m, =,..., m. In our study, we still mainly focus on the central region. The bloced standard deviation of abor feature is calculated as follows: / m m ( ) = g g, g = g m = m =. () Since the central region consists of only the foreground blocs, we needn t perform fingerprint segmentation before abor filtering. The quality score of a fingerprint can be computed by summing the standard deviation of all the blocs. 0

7 78 D. Yu et al. Q N 3 = ( i) C3 i=. () where C 3 is also a normalizing constant ranging the quality score form 0 to. In summary, Q 3 is aimed at assessing the orientation properties of fingerprint, strong orientation relates to good quality. The quality estimation by Q 3 can also be depicted as follows: ) Locate the core point of the fingerprint image. ) Divide the central region into N blocs of size w w. 3) For each bloc centered at pixel (i,j), compute the m abor features and standard deviation value by equations (0) and () respectively. 4) Obtain the quality score Q 3 by summing standard deviation value of all the blocs using equation (). 3 Fusion Criterion and Quality Scoring In this section, we will present a novel fusion criterion combined the three evaluation results mentioned before. Since each method has its advantages and drawbacs, our research mainly aims at finding an optimal fusion criterion maing benefits of the above assessment. We define a fusion criterion which calculates a quality score Q according to: 3 Q = wiqi i, i =, C i=. (3) where C is a normalizing constant which ranges the quality score in [0,], w i denotes the weight of each quality score Q i, and i is a power factor of each quality score Q i. The above three features contribute differently to fingerprint quality evaluation. The fingerprint images of three categories are labeled as two inds good quality and bad quality images merging the original wet and dry fingerprints. The values of Fourier and abor features are large to good fingerprint images. While, the value of R B of central region is abnormal to bad fingerprint images, Q can efficiently detect the bad quality images, so the weight of Q will be bigger to bad quality images than others, and and 3 are set to, and respectively, while Q and Q 3 can quantify the good quality images very well, the weights of these two scores are bigger than Q, and 3 are set to, and respectively. Based on the above analysis, the fusion quality score Q is defined as equation (3). As the contribution of each feature to the final quality score is nonlinear, the above equation can perform a better classification of good and bad images than the linear method, which will be given in section 4. 4 Experimental Results The fingerprint database used in this experiment consists of 6 fingerprint images. The size of each fingerprint image is pixels with the resolution of 500dpi

8 Fusion Method of Fingerprint Quality Evaluation 783 and 56 gray levels. And our research focuses on the central region, the size of each bloc is 6 6 pixels. We verify the evaluation performance of our method on this public security fingerprint database using blac pixel ratio of central area, directional abor feature and ring structures of spectrum. Fig. 3 shows the image score distribution of the three different feature evaluation methods and the score distribution of the fusion approach. As shown in Fig. 3, the abor feature method and Fourier spectrum method can easily classify the fingerprint images into two groups: good and bad quality. The central area blac white pixel ratios method is able to detect bad fingerprint images whose blac pixel ratio of central area is abnormal. To some images, though the abor feature score and Fourier spectrum score are high, the central area score is low. Under this similar circumstance, the fusion method is needed for getting a reasonable score. Fig. 3 demonstrates that the fusion method can find the poor quality images easily. After observing the images, we find that those fingerprint images whose final score are less than 0. have very bad quality. In order to test our proposed fusion method, we have sent these images to fingerprint classification system [8]. With ten percents fingerprint images of poor quality rejected, the five-classification (whorl, right loop, left loop, arch and tented arch) accuracy can be increased from 90.6 percent to 94.3 percent. Fig. 4 show the score distribution of our nonlinear fusion method and the linear method [7]. From the figure, we can find that nonlinear method show better performance of distinguishing good and bad fingerprint images as described in section 3. In order to compare with the results of linear method, we have also sent these images to fingerprint classification system [8]. Based on the linear method, we reject ten percents fingerprint images of poor quality, the five-classification accuracy can be increased from 90.6 percent to 9.5 percent. We can get the conclusion that fingerprint classification based on our fusion method shows.8 percent better classification accuracy than the linear method. By rejecting 5 percent images of bad quality according to our fusion method of fingerprint quality evaluation, the fingerprint classification accuracy will be increased to 96 percent. Fig. 3. Fingerprint image quality score distribution of different methods

9 784 D. Yu et al. Fig. 4. Fingerprint image quality score distribution of nonlinear and linear methods (a) (b) (c) Fig. 5. Typical Fingerprint images: (a) good fingerprint; (b) dry fingerprint; (c) wet fingerprint Fig.5 shows three typical fingerprint images. Based on our fusion method of fingerprint quality evaluation, fingerprint Fig.5 (a) whose contrast between ridges and valleys is high has the best score of all the fingerprints, fingerprint Fig.5 (b) with largely corrupted ridge structure and low contrast between ridges and valleys is the worst of all the dry fingerprints, and fingerprint Fig.5 (c) with low gray-level and low contrast ridges and valleys has the lowest score of the wet fingerprints. 5 Conclusions We have developed a fusion method combining three features for quality evaluation of fingerprint image, which gives better performance than linear methods. Our experimental results demonstrate that the three features were sufficient for detecting poor quality fingerprint images. However, the proposed method relies on the correctly located core point and the foreground central region. Further researches will emphasize on a more accurate core point detection for fusion method to improve the performance of the fingerprint image quality evaluation.

10 Fusion Method of Fingerprint Quality Evaluation 785 Acnowledgement We would lie to acnowledge the supports of China NNSF of excellent Youth (603530), NNSF ( )& DNSF/DCNLF ( /CN0040). References. Lin Hong, Yifei Wan, and Anil Jain, Fingerprint image enhancement: algorithm and performance evaluation, IEEE Trans. Pattern Analysis Machine Intelligent, Vol.0, No. 8, pp , August 998. Nalini K. Ratha and R. Bolle, Fingerprint image quality estimation, ACCV, PP.89-83, L.L. Shen, A. Kot and W.M. Koo, Quality measure of fingerprint images, Third International Conference, AVBPA 00, Halmstad, Sweden, Proceedings, pp. 66-7, Jun, E. Lim, X. Jiang, W. Yau, Fingerprint quality and validity analysis, IEEE ICIP, Bongu Lee, Jihyun Moon, and Hail Kim, A novel measure of fingerprint image quality using Fourier spectrum Proc. SPIE Vol. 5779,05(005) 6. Jin Qi, Zhongchao Shi, Xuying Zhao and Yangsheng Wang, Measuring fingerprint image quality using gradient Proc. SPIE Vol. 5779,455(005) 7. Qi, J., Abdurrachim, D., Li, D., Kunieda, H, A hybrid method for fingerprint image quality calculation, Automatic Identification Advanced Technologies, 005. Fourth IEEE Worshop on 7-8 Oct. 005 Page(s): A.K. Jain, S. Prabhaar and H. Lin, A Multichannel Approach to Fingerprint Classification, IEEE Trans. Pattern Analysis and Machine Intelligence, vol., no. 4, pp , 999.