Author name used in this publication: David ZhangVersion of RecordPublishe

Wang, K

Wu, X

Zhang, DD

Author name used in this publication: David ZhangVersion of RecordPublishedVoR allowe

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Palmprint Texture Analysis Using Derivative of Gaussian Filters∗Xiangqian Wu, Kuanquan WangSchool of Computer Science and TechnologyHarbin Institute of Technology (HIT)Harbin 150001, China{xqwu, wangkq}@hit.edu.cnDavid ZhangDepartment of ComputingHong Kong Polytechnic UniversityKowloon, Hong Kong, Chinacsdzhang@comp.polyu.edu.hkAbstractThis paper presents a novel approach of palmprint tex-ture analysis based on the derivative of gaussian ﬁlter. Inthis approach, the palmprint image is respectively prepro-cessed along horizontal and vertical direction using deriva-tive of gaussian (DoG) Filters. And then the palmprint isencoded according to the sign of the value of each pixel ofthe ﬁltered images. This code is called DoGCode of thepalmprint. The size of DoGCode is 256 bytes. The simi-larity of two DoGCode is measured using their Hammingdistance. This approach is tested on the PolyU PalmprintDatabase, which containing 7605 samples from 392 palms,and the EER is 0.19%, which is comparable with the exist-ing palmprint recognition methods.1. IntroductionComputer-aided personal recognition is becoming in-creasingly important in our information society. Biometricsis one of the most important and reliable methods in thisﬁeld [1,2]. Within biometrics, the most widely used biomet-ric feature is the ﬁngerprint [3, 4] and the most reliable fea-ture is the iris [2, 5]. However, it is very difﬁcult to extractsmall unique features (known as minutiae) from unclear ﬁn-gerprints [3,4] and the iris input devices are expensive. Thepalmprint is a relatively new biometric feature. Comparedwith other currently available features, palmprint has sev-eral advantages [6]. Palmprints contain more informationthan ﬁngerprints, so they are more distinctive. Palmprintcapture devices are much cheaper than iris devices. Palm-prints contain additional distinctive features such as prin-cipal lines and wrinkles, which can be extracted from low-∗This work is supported by the National Natural Science Foundationof China (No. 60441005), the Key-Project of the 11th-Five-Year Plan ofEducational Science of Hei Longjiang Province, China (No. HZG160)and the Development Program for Outstanding Young Teachers in HarbinInstitute of Technology.resolution images. By combining all features of palms, suchas palm geometry, ridge and valley features, and principallines and wrinkles, it is possible to build a highly accuratebiometrics system.Many algorithms have been developed for palmprintrecognition in the last several years. Han [7] used Sobel andmorphological operations to extract line-like features frompalmprints. Similarly, for veriﬁcation, Kumar [8] used otherdirectional masks to extract line-like features. Wu [9] usedFisher’s linear discriminant to extract the algebraic feature(called Fisherpalms). The performance of these methodsare heavily affected by the illuminance. Zhang [10, 11]used 2-D Gabor ﬁlters to extract the texture features (calledPalmCode) from low-resolution palmprint images and em-ployed these features to implement a highly accurate onlinepalmprint recognition system. In this paper, we encodeda palmprint using the derivative of gaussian (DoG) Filter.This code is called DoGCode. In the matching stage, theHamming distance is used to measure the similarity of theDoGCodes.When palmprints are captured, the position, directionand amount of stretching of a palm may vary so that evenpalmprints from the same palm may have a little rotationand translation. Furthermore, palms differ in size. Hencepalmprint images should be orientated and normalized be-fore feature extraction and matching. The palmprints usedin this paper are from the Polyu Palmprint Database [12].The samples in this database are captured by a CCD basedpalmprint capture device [11]. In this device, there are somepegs between ﬁngers to limit the palm’s stretching, transla-tion and rotation. These pegs separate the ﬁngers, form-ing holes between the foreﬁnger and the middle ﬁnger, andbetween the ring ﬁnger and the little ﬁnger. In this pa-per, we use the preprocessing technique described in [11]to align the palmprints. In this technique, the tangent ofthese two holes are computed and used to align the palm-print. The central part of the image, which is 128 × 128,is then cropped to represent the whole palmprint. Such pre-processing greatly reduces the translation and rotation of the1-4244-0605-6/06/$20.00 ©2006 IEEE. 751Authorized licensed use limited to: Hong Kong Polytechnic University. Downloaded on March 29, 2009 at 21:42 from IEEE Xplore.  Restrictions apply.palmprints captured from the same palms. Figure 1 showsa palmprint and its cropped image.(a) Original Palmprint (b) Cropped ImageFigure 1. An example of the palmprint and itscropped image.2. DoGCode ExtractionLet I denote a palmprint image and Gσ denote a 2DGaussian ﬁlter with the variance σ. Denote Gσx and Gσyas the derivative of Gσ along the x and y directions. Thepalmprint is ﬁrst ﬁltered by Gσx as below:Ix = I ∗Gσx (1)Iy = I ∗Gσy (2)where ∗ is the convolution operator.Then the palmprint is encoded according to the sign ofeach pixel of Ix and Iy:Cx(i, j) ={1, if Ix(i, j) > 0;0, otherwise.(3)Cy(i, j) ={1, if Iy(i, j) > 0;0, otherwise.(4)C = (Cy, Cx) is called DoGCode of the palmprint I .The size of the preprocessed palmprint is 128× 128. Extraexperiments shows that the image with 32×32 is enough forthe DoGCode extraction and matching. Therefore, beforecompute the DoGCode, we resize the image from 128×128to 32 × 32. Hence the size of the DoGCode is 32 × 64.Figure 2 shows some examples of DoGCode.3 Similarity Measurement of DoGCodeBecause all DoGCodes have the same length, we canuse Hamming distance to deﬁne their similarity. Let C1 =(C1x, C1y), C2 = (C2x, C2y) be two DoGCodes. Themodiﬁed Hamming distance of C1 and C2, denoted as(a) (b)(c) (d)(e) (f)(g) (h)Figure 2. Some examples of DoGCodes. (a)and (b) are two palmprint samples from apalm; (c) and (d) are two palmprint samplesfrom another palm; (e)-(h) are the DoGCodesof (a)-(d), respectively.1-4244-0605-6/06/$20.00 ©2006 IEEE. 752Authorized licensed use limited to: Hong Kong Polytechnic University. Downloaded on March 29, 2009 at 21:42 from IEEE Xplore.  Restrictions apply.(H(C1, C2)), is deﬁned as following, i.e.H(C1, C2) =32∑i=132∑j=1[(C1x(i, j)⊗ C2x(i, j))∧(C1y(i, j)⊗ C2y(i, j))](5)where ⊗ and ∧ are the logical XOR and AND operation.The matching score of two DoGCodes C1 and C2 is thendeﬁned as below:S(C1, C2) = 1− H(C1, C2)32× 32 (6)Obviously, S(C1, C2) is between 0 and 1 and the largerthe matching score, the greater the similarity between C1and C2. The matching score of a perfect match is 1. Thematching score of a perfect match is 1. Because of imper-fect preprocessing, there may still be a little translation be-tween the palmprints captured from the same palm at dif-ferent times. To overcome this problem, we vertically andhorizontally translate C1x and C1y a few points to get thetranslated C1, and then, at each translated position, com-pute the matching score between the translated C1 and C2.Finally, the ﬁnal matching score is taken to be the maxi-mum matching score of all the translated positions. Table 1lists the matching scores between the samples in Figure 2.From this table, the matching scores of the DoGCodes ofthe palmprints from the same palm are much greater thanthat from the different palms.Table 1. The Matching Scores between theDoGCodes in Figure 2Figure 2 (e) (f) (g) (h)(e) 1 0.7607 0.3570 0.3419(f) - 1 0.3208 0.3258(g) - - 1 0.8448(h) - - - 14 Experimental ResultsWe employed the PolyU Palmprint Database [12] to testour approach. This database contains 7605 grayscale im-ages captured from 392 different palms by a CCD-baseddevice. These palmprints were taken from people of dif-ferent ages and both sexes and were captured twice, at aninterval of around two months, each time taking about 10images from each palm. Therefore, This database containsabout 20 images of each palm. The images are of two differ-ent sizes, 384× 284 and 768× 568. In our experiments, allimages were resized to 384×284 and, using the preprocess-ing technique described in [11], the central 128 × 128 partof the image was cropped to represent the whole palmprint.Some typical samples in this database are shown in Figure3, in which the last two samples were captured from thesame palm at different sessions. According to this ﬁgure,the lighting condition in different sessions is very different.(a) (b)(c) (d)Figure 3. Some typical samples in the PolyuPalmprint Database.0 0.2 0.4 0.6 0.8 100.010.020.030.040.050.060.07Matching ScoresPercent (%)GenuineImpostorFigure 4. The Distributions of Genuine andImpostor Matching Scores.In order to investigate the performance of the proposedapproach, each sample in the database is matched againstthe other samples. The matching between palmprints whichwere captured from the same palm is deﬁned as a genuinematching. Otherwise, the matching is deﬁned as an im-postor matching. A total of 28, 914, 210 (7605 × 7604/2)1-4244-0605-6/06/$20.00 ©2006 IEEE. 753Authorized licensed use limited to: Hong Kong Polytechnic University. Downloaded on March 29, 2009 at 21:42 from IEEE Xplore.  Restrictions apply.matchings have been performed, in which 141, 004 match-ings are genuine matchings. Figure 4 shows the genuine andimpostor matching scores distribution. There are two dis-tinct peaks in the distributions of the matching scores. Onepeak (located around 0.7) corresponds to genuine match-ing scores while the other peak (located around 0.3) corre-sponds to impostor matching scores. These two peaks arewidely separated and the distribution curve of the genuinematching scores intersects very little with that of impos-tor matching scores. Therefore, the proposed approach canvery effectively discriminate between palmprints.The Receiver Operating Characteristic (ROC) curve ofthe proposed approach, which plots the pairs (FAR, FRR)with different thresholds, is shown in Figure 5. For compar-isons, the FusionCode method [10], which is an improve-ment of the PalmCode algorithm [11], is also implementedon this database. In the FusionCode method, each sample isalso matched with the others. The ROC curve of the Fusion-Code method is plotted in Figure 5 and the correspondingequal error rates (EERs) are listed in Table 2. Accordingto the ﬁgure, the whole curve of the DoGCode approach isbelow that of the FusionCode method, which means thatthe performance of the proposed approach is better thanthat of the FusionCode method. From Table 2, the EERof the DoGCode approach is 0.19%, which is much smallerthan that of FusionCode (0.56%). Furthermore, the size ofa DoGCode is (32× 64)÷ 8 = 256 bytes, which is 2/3 ofthe size of the FusionCode.0 1 2012False Accept Rate (%)False Reject Rate (%)EERFusionCodeDoGCodeFigure 5. The ROC Curve of the Proposed Ap-proach and FusionCode Method.5 ConclusionA novel approach to palmprint authentication is pre-sented in this paper. The palmprint DoGCode is extractedTable 2. Comparisons of Different PalmprintRecognition MethodsMethod DoGCode FusionCode [10, 11]EER (%) 0.19 0.56Feature Size (bytes) 256 384using derivative of gaussian Filters. The similarity of theDoGCode is deﬁned using their Hamming distance. Ac-cording to the experimental results, the DoGCode approachneeds less storage and gets a much higher accuracy thanone of the most powerful palmprint recognition method—FusionCode.References[1] A. Jain, R. Bolle, and S. Pankanti. Biometrics: PersonalIdentiﬁcation in Networked Society. Kluwer Academic Pub-lishers, 1999.[2] D. Zhang. Automated Biometrics–Technologies and Sys-tems. Kluwer Academic Publishers, 2000.[3] A. Jain, L. Hong, and R. Bolle. On-line ﬁngerprint veriﬁca-tion. IEEE Transactions on Pattern Analysis and MachineIntelligence, 19(4):302–313, 1997.[4] D. Maio, D. Maltoni, R. Cappelli, J. L. Wayman, andA. Jain. FVC2000: Fingerprint veriﬁcation competition.IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 24(3):402–412, 2002.[5] R. Wildes. Iris recognition: an emerging biometric technol-ogy. Proceedings of the IEEE, 85(9):1348–1363, 1997.[6] A. Jain, A. Ross, and S. Prabhakar. An introduction to bio-metric recognition. 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IEEE Transactions on Pattern Analysis andMachine Intelligence, 25(9):1041–1050, 2003.[12] PolyU Palmprint Palmprint Database.http://www.comp.polyu.edu.hk/∼biometrics/.1-4244-0605-6/06/$20.00 ©2006 IEEE. 754Authorized licensed use limited to: Hong Kong Polytechnic University. Downloaded on March 29, 2009 at 21:42 from IEEE Xplore.  Restrictions apply.

Palmprint texture analysis using derivative of gaussian filters

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