Qua lity C la ssif ica tion M ethod for F ingerpr in t Image Ba sed on Support Vector M ach ine

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1 22 1 Vol. 22 No PR & A I Feb ( ),.,,.. Gabor,. ( SMOTE)..,,,,( SMOTE) TP Qua lity C la ssif ica tion M ethod for F ingerpr in t Image Ba sed on Support Vector M ach ine ZHANG Yu, YIN Yi2Long, LUO Gong2Q ing (School of Com puter Science and Technology, Shandong U niversity, J inan ) ABSTRACT In an automatic fingerp rint identification system, estimating the quality of fingerp rint image has significant value for segm entation, enhancement and matching p rocesses. B esides, the quality classification of fingerp rint image is of paramount significance in the app licability research of fingerp rint recognition algorithm. In this paper, a method for quality classification of fingerp rint image is p roposed based on the support vector machine ( SVM ). The gradient, Gabor feature, and directional contrast are used as the quality index, and SVM is app lied to achieve quality classification of fingerp rint image. M eanwhile, synthetic m inority over samp ling technique ( SMOTE) method is emp loyed to reduce the influence of class imbalance p roblem. Both the theoretical analysis and the experimental results indicate the validity of the p roposed m ethod. Key W ords Fingerp rint, Image Quality, Quality Classification, Support Vector Machine, Synthetic M inority Over Samp ling Technique ( SMOTE) 3 (No ) (No. 2006BS01008) (No. 2005GG ) (No. 2007ZCB01030) : ; : ,, 1981,,.,, 1972,,,. E2mail: ylyin@ sdu. edu. cn.,, 1979,,.

2 [ 1-2 ],.,,.,.,. Yun [ 3 ],. Fierrez - Aguilar [ 4 ]. Yi Chen [ 5 ],,, FVC2002DB3, 1. 94%.,. [ 6 ] : ( ),. [ 7 ].,.. 5 [ 8 ] ;... 1) [ 5, 9-12 ].. Yi Chen [ 5 ],. Hong [ 9 ] Shen [ 10 ] Gabor,. [ 11 ] [ 12 ].,. 2) [ 5-13 ].. Yi Chen [ 5 ],. Ratha Bolle [ 13 ] (W avelet Scalar Quantiza2 tion, W SQ).., ;.,., Q i [ 14 ]., , ( ). 3. 1) [ 5 ]. b b, B, g s = ( g x s, g y s ) s B. b 2 J = 1 b 2 g s g T s j 11 j 12. s B j 21 j 22 : 1 = 1 2 ( trace (J ) + trace2 (J ) - 4det (J ) ), 2 = 1 2 ( trace (J ) - trace2 (J ) - 4det (J ) ), trace (J ) = j 11 + j 12, det (J ) = j 11 j 22 - j 2 12, 1 2. : k = ( 1-2 ) 2 = ( j j 22 ) + 4 j 2 12, 0 k 1. ( ) 2 ( j 11 + j 22 ) 2 B 2. 2, 1 µ 2.,, 1 2,, k 0.,, Q s = 1 r r w i k i, i =1, r, l i w i w i = exp{ - l i - l c 2 / (2q) }, = ( x i, y i ) i

3 1 : 131, l c ; q,.,,., 1. 1, ,,. Q I < T Q,,,,T q T Q., 2. 2, ,. Gabor, ( ) Gabor, Gabor,. 1 Fig. 1 Score distribution by using gradient as quality index 2) Gabor [ 10 ]. Gabor, h ( x, y, k, x, y ) = x k exp [ - 2 k 1 2 ( x 2 x = xcos k + y sin k, y k k = 1, 2,, m. + y2 k 2 y ) ] exp ( i2 fx k ), = - x sin k + ycos k, [ 23 ] f, g (X, Y, k, f, x, y ) = w /2-1 w /2-1 x = - w /2 y = - w /2 I (X + x, Y + y) h ( x, y, k, f, x, y ), k = 1, 2,, m. Gabor. G = ( Gabor : 1 m ( g m - 1 k - g ) 2 ) 1 /2, g = 1 m g k. k =1 m k =1, Gabo r.,gt q,,. Q I =, 2 Gabor Fig. 2 Score distribution by using Gabor feature as quality index 3) [ ].,.,,,.,, 3. ( a) 8 ( b) ( a) 8 directional filter ( b) Filter operation of directional contrast 3 Fig. 3 Sketchmap for directional contrast method

4 : S i ( x, y) i max D k 8 = x =1 y =1 = 2 G ( P ij ), i = 1, 2,, 8; j =1 8 = max ( i ) ; S i ( x, y) ; = max - k, k = 1, 2,, N; S DC = 1 c 2 D k ; N k =1, G ( P ij ) P ij, S i ( x, y) i, i i, N, max, D k k, S DC., 4. 4, 17 27,.,.,,. 4 Fig. 4 Score distribution by using directional contrast as quality index, ( 1 2 4),.,..,,,,., 3,, ( Gabor),. 5.,,,,.,,. 5 Fig. 5 Index distribution of fingerp rint images of different quality 2. 2,. 25%,.,,,,. ( Synthetic M inority Over Samp ling Technique, SMOTE),.. 3 SMOTE 3. 1 [ ] VC ( Structural R isk M inim iza2 tion, SRM ),,,,.

5 1 : SVM,.,,,.,,.,, (). SVM,, SMO TE,,.,, ( ).,,.,.,., [ ].., : 1) (Over2Samp ling), ; 2)(Under2Sam2 p ling),.,.,, ( )SMOTE [ 22 ].. 4 : 1)., ; 2)., ,., 125,75., ; ( a) ( a) Low quality ( d) ( d) H igh quality 6 Fig. 6 Examp les for fingerp rint imageswith different quality. step 1. Ga2 bor,. step 2SVM. SVM, SVM [ 24 ] ( [ 25 ] ). 1. step 3. SMOTE. K = 2, i, i,., 1 1,. step 4 SVM.,. 1.,,, 92. 5%,.

6 134 22,,,SVM Gabor,., SVM,. 1 Table 1 Correct rates of different classification methods Gabor SVM SMOTE + SVM %,,,,. SVM,90. 7%. 5,., SMOTE.,,,.,.,,..,... [ 1 ] Galton F. Finger Prints. New York, USA: Da Capo Press, 1961 [ 2 ] Lee H C, Gaensslen R E. Advances in Fingerp rint Technology. New York, USA: Elsevier, 1991 [ 3 ] Yun E K, Cho S B. Adap tive Fingerp rint Image Enhancement with Fingerprint Image Quality Analysis. InternationalVacuum Congress, 2006, 24 (1) : [ 4 ] Julian F A, Chen Yi, JavierO G, et al. Incorporating Image Quality in Multi2A lgorithm Fingerprint Verification / / Proc of the Interna2 tional Conference on B iometrics. Hongkong, China, 2006: [ 5 ] Chen Yi, Dass S C, Jain A K. Fingerp rint Quality Indices for Pre2 dicting Authentication Performance / / Proc of the 5 th International Conference on Audio2and V ideo2based B iometric Person Authentica2 tion. H ilton Rye Town, UK, 2005: [ 6 ] Jain A K, Prabhakar S, Hong L in, et al. Filterbank2Based Finger2 printmatching. IEEE Trans on Image Processing, 2000, 9 ( 5 ) : [ 7 ] Marana A N, Jain A K. R idge2based Fingerp rint Matching U sing Hough Transform / / Proc of the XV IIIB razilian Symposium on Com2 puter Graphics and Image Processing. Natal, B razil, 2005: [ 8 ] Jain A K, Prabhakar S, Hong L in. A Multichannel App roach to Fingerprint Classification. IEEE Trans on Pattern Analysis and Ma2 chine Intelligence, 1999, 21 (4) : [ 9 ] Hong L in, W an Yifei, Jain A. Fingerp rint Image Enhancement: A l2 gorithm s and Performance Evaluation. IEEE Trans on Pattern Analy2 sis and Machine Intelligence, 1998, 20 (8) : [ 10 ] Shen L inlin, Kot A, Koo W M. Quality Measures of Fingerprint Images / / Proc of the 3rd International Conference on Audio2and V ideo2based B iometric Person Authentication. Halm stad, Sweden, 2001: [ 11 ] Ballan M, Sakarya F A, Evans B L. A Fingerp rint Classification Technique U sing D irectional Images / / Proc of the 31st A silomar Conference on Signals, System s & Computers. Pacific Grove, USA, 1997, : [ 12 ] JiangW C. An Adaptive Feature Extraction A lgorithm forautomat2 ic Fingerp rint Recognition. MasterD issertation. Seoul, Korea: Yon2 sei University. School of Electrical and Electronic Engineering, 2002 [ 13 ] Ratha N, Bolle R. Fingerp rint Image Quality Estimation. IBM Computer Science Research, Report RC21622, Yorktown Heights, USA: IBM T. J. W atson Research Center, 1999 [ 14 ] Q i Jinqing, Abdurrachim D, L i Dongju, et al. A Hybrid Method for Fingerprint Image Quality Calculation / / Proc of the 4th IEEE Workshop on Automatic Identification Advanced Technologies. Buf2 falo, USA, 2005: [ 15 ] Lee B, Moon J, Kim H. A Novel Measure of Fingerp rint Image Quality U sing Fourier Spectrum / / Proc of the Conference on B io2 metric Technology for Human Identification. O rlando, USA, 2005:

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