Blurred Palmprint Recognition Based on Relative Invariant Structure Feature

Transcription

1 5 International Conference on Comptational Science and Comptational Intelligence Blrred Palmprint Recognition Based on Relative Invariant Strctre Featre Gang Wang, Weibo Wei*, Zhenkan Pan College of Information Engineering,Qingdao University, Qingdao, China Abstract A blrred palmprint recognition method based on Relative Invariant Strctre Featre (RISF) is proposed in this paper to improve the lo recognition accracy of blrred palmprint. Firstly, the OSV decomposition model is sed to obtain stable featre from blrred images. Next, a nonoverlapping sampling method based on Strctre Ratio (SR) for RISF is sed to frther improve the effectiveness of featre. Finally, Strctral Similarity Index Measrement (SSIM) is introdced to measre the similarity of palmprints and jdge the palmprint category for classification. Nmerical experiments sho that the proposed method is effective and better than some other classical algorithms. Keyords Blrred palmprint recognition; Relative Invariant Strctre Featre; OSV decomposition model; Strctre Ratio I. INTRODUCTION Palmprint is one important biometric featre. Palmprint recognition can be classified into three categories according to featre description and matching, inclding principal line extraction, sbspace learning, and textre coding. The principal line extraction method [,] depends on the ridge information extraction sing edge detection method. Palmprint image is considered as high-dimensional data in sbspace learning [,4], and the high-dimensional data is mapped to lo-dimensional space sing characteristic of matrix, the method have been sccessflly applied to face recognition, and also achieved good reslts hen transplanted to the palm. The textre coding method [5,6] se a filter for palmprint image filtering, then encode them according to different schemes, finally, and or XOR operator is sed to calclate the similarity beteen different featres. The above-mentioned three methods sally se clear palmprint image captred by contact device, hoever, system secrity is also very important for identification, as a reslt, non-contact palmprint image acqisition and recognition research become the mainstream de to its varios merits, e.g. lo-cost, easy availability, high accracy [7,8], nfortnately, defocs stats might blr palmprints hich as captred sing non-contact device, the obtained blr palmprint is easy to degrade the performance of the recognition system. Researchers proposed some methods to solve this problem, Yan [9] introdces image clarity evalation standards to obtain the clear palmprint image, bt it cannot be sed in large-scale palmprint database. Kang [] sed a template convoltion to calclate the image focs vale and set threshold vale based on the focs vale. Wang [] proposed an image restoration method based on normalized sper Laplace, hich achieves a good experimental reslt. Sang [] proposed a blrred palmprint recognition algorithm based on to-dimensional principal component analysis, hich offers an effective ay for frther research on blrred palmprint recognition. Lin [] state that stable featres exist dring the process from clear image to blrred images, and they extracted stable featres by sing Laplacian Smoothing Transform (LST), and achieved good recognition reslts hen selecting the N lofreqency elements as featre vectors to recognize, bt they did not explain ho to select stable featres. In order to effectively extract the stable featres in palmprint blrred process, a blrred palmprint recognition method based on Relative Invariant Strctre Featre (RISF) as proposed. Finally, nmerical experiments sho that the proposed algorithm is effective and better. II. RELATED WORKS A. OSV Decomposition Model In, Osher et al described the textre and noise v in H ( Ω) spatial domain and proposed OSV image decomposition model, hich can be expressed as {E( ) = dxdy ( Ω Δ ( )) dxdy} λ Ω f () Eler-Lagrange eqation can be obtained by (): = f λδ, in Ω (), = =, on Ω n n Eqation () is a forth-order eqations ith comptational complexity and exist convergence speed problem, ε is commonly sed to approximate, here ε. Hoever, in some cases, is very sensitive to the parameters ε, namely, if ε is too large, it ill blr the edges of image; If ε is too small, e may get an error nmerical soltion. Therefore, e old not get image decomposition effectively. A staircase effect is exist for those denoised images by sing ROF model, reslting in the loss of the image information. Chan and Esedogl [] replaced the data item of ROF model ith Ω f dxdy, this model effectively improved the loss of geometric featres. Therefore, this paper introdces the improved data item into OSV model. Eqation () can be reritten as /5 $. 5 IEEE DOI.9/CSCI

2 E( ) dxdy ( = Ω Δ ( )) dxdy λ Ω f In order to redce the comptational complexity and improve convergence rate, e propose Mltiple Axiliary Variable Method (MAVM) and introdce it into (). More specifically shon in the folloing ( )( f ) Δ = f { E(,,, ) = Ω dxdy dxdy λ Ω ( b k ) dxdy θ Ω,,, ( f Δ ) dxdy θ Ω ( ) dxdy θ Ω (4) here is a scalar,, b are vectors. k k k k b = b (5) The soltion for (4) can be converted into the folloing fnctional problems, therefore, (4) can be reritten as { E ( ) ( ) = b k dxdy θ Ω Ω ( f Δ ) dxdy θ (6) () E ( ) = dxdy Ω ( k b (7) ) dxdy θ Ω E ( ) = ( ) f Δ dxdy θ Ω (8) ( ) dxdy θ Ω { E ( ) ( ) } 4 = dxdy dxdy λ Ω θ Ω (9) The corresponding soltions are obtained as follos k k θ = f Δ ( k k k Δ b ) () θ ( k b k k k k = Max b θ,) () k b k θ ( ) k θ Δ Δ k Δ =Δ Δf θ () θ k k ( k θ,) = Max () λ k Where () se an explicit iteration method, () se a semi-implicit iterative method, () and () ses a soft threshold formla to evalate the analytical soltion. The algorithm is described as follos: Initialization = = = = = b = b = = f ; Iterationreplaced f ith k complete alternative iteration of (6), (), (), ()and () Iterative teration criteria stop rnning if k k satisfied ith max( ) ε. B. Reslts of Image Decomposition In order to sho the strctre layer and textre layer obviosly, OSV decomposition model is sed for palmprint image. Fig. and Fig. shos that the strctre layer remains nchanged ith the degree of blrσ becomes larger and larger, the textre layer hich changes dramatically, namely, the information in the textre layer is nstable. (a) original (b) σ = (c) σ = (d) σ = (e) σ =4 (f) σ =5 Figre. The strctre layer ith different degrees of blrring corresponding to Figre. obtained sing the OSV decomposition model (a) original (b) σ = (c) σ = (d) σ = (e) σ =4 (f) σ =5 Figre. The textre layer ith different degrees of blrring corresponding to Figre. obtained sing the OSV decomposition model Fig. and Fig. 4 sho the three-dimensional srface plots of strctre layer and textre layer corresponding to Fig. and Fig., hich can be clearly observed the changes for strctral layer and the textre layer hen blrring. As

3 shon in Fig., strctre layer contains main information of the image, Fig.4 shos that the featre is extremely nstable in the process of image blr. Hereby it can not be regarded as the stable image featres. According to the reslts of OSV decomposition, e kno that the strctre layer remains stable even if an image sffers from different degree of blrring. As a reslt, the strctre layer of the image can be treated as a stable featre to recognize. (a) original (b) σ = (c) σ = Ratio (SR) for RISF to frther improve the discriation and effectiveness of featre, and obtaining the SR-RISF. This can be expressed as m n I ( i, j ) i= j= M = m n m n ( I(, i j) M ) i= j= S = m n M SR S m n (4) (5) = (6) here I is a size of non-overlapping sampling area of original palmprint image, i and j is corresponding pixel location of sampling area, M stands for grey average of sampling area, S is variance of sampling area, SR is strctre ratio of sampling area. We select the strctre layer of blrring palmprint image ( σ = ) to test by sing SR method, reslts can be seen from Fig. 5, it is difficlt to visally distingish the blrring image beteen A and B. (d)~(f) is the RISF obtained by OSV decomposition model, hich cold better distingish the similar blrring image, (g)~(i) is the SR-RISF featre hich needs frther improve the discriation of featres for the palmprint image. (d) σ = (e) σ =4 (f) σ =5 Figre. Srface plots of the strctre layers corresponding to Figre (a) The palmprint for A (b) The palmprint for A (c) The palmprint forb (a ) original (b ) σ = (c ) σ = (d) RISF to (a) (e) RISF to (b) (f) RISF to (c) G V G V G V (d ) σ = (e ) σ =4 (f ) σ =5 Figre 4. Srface plots of the textre layers corresponding to Figre III. RECOGNITION ALGORITHM BASED ON SR-RISF A. Image Don-sampling Based on Strctre Ratio We take the strctre layer as the Relatively Invariant Strctral Featres (RISF) in the process of image blrring. Althogh the strctre layer can keep relatively stability, the identification reslts are not good enogh. Therefore, e se a non-overlapping sampling method based on Strctre Y X Y (g) SR-RISF to (a) (h) SR-RISF to (b) (i) SR-RISF to (c) Figre 5. Blrred palmprint featre examples among the same people and the different people B. Featre Matching for Blrring Palmprint Image Strctral Similarity Index Measrement (SSIM) [4] as introdced in this paper to measre the similarity beteen palmprint featres. SSIM algorithm can describe X Y X

4 the strctre featres from three aspects hich are the bright (L), contrast(c) and strctre(s). Specifically shon as follos μxμy c Lxy (, ) = μ x μy c σxσy c C( x, y) = σ x σy c σ xy c S( x, y) = σxσy c (7) (8) (9) Where x and y represent to strctral featres to be matched, μx and μ y stand for to the average vale of x and y, σ x and σ y stands for the variance of x and y, σ xy is the covariance of x and y, small constants c, c, c are sed to increase the stability of calclation reslts. We sally take c = ( K GVM), c = ( K GVM) c= c/ K << K <<, GVM is the maximm grey vale of palmprint image (typically take 55), SSIM vale is detered by the vales of L, C, S. SSIM( xy, ) = Lxy (, ) Cxy (, ) S( xy, ) () Where SSIM vale represents the similarity of to matched strctral featre, the range is from to. Therefore, e shold set a threshold vale thres so that to palmprint image can be classified. conveniently compared ith different algorithms. We carried ot experiments on PolyU palmprint database and Blrred- PolyU palmprint database in order to verify the effectiveness of the proposed method. A. Experiment It is difficlt to describe the characteristics if e directly se the strctre layer as the characteristics to recognize, therefore, the size of the sampling area has a great inflence on the final recognition reslts hen e extract the featre of blrred palmprint image, and the optimal partitioning method are sally detered by experiment. Therefore, blocks of different size ( ) are tested in the Blrred-PolyU palmprint database to choose the optimal regional block. Fig. 7 shos the ROC crve of different size sampling area for SR-RISF algorithm and RISF algorithm. FRR(%) SR-RISF *) SR-RISF 6*6) SR-RISF 8*8) SR-RISF 4*4) RISF Figre 7. ROC crves obtained sing different block size Figre 6. Crves of inter-class and intra-class As can be seen from Fig.6, the vale of inner-class crve of SSIM concentrates on the near side of the, the closer to, the higher similar of to blrred palmprint image, and the vale of intra-class crve of SSIM concentrated on the near side of the, the closer to, the bigger difference beteen to blrred palmprint image. Therefore, an appropriate threshold of thres (e can obtained the size of threshold by eqal error rate crve [5], thres =.45 ) can separate the palm more accrately and effectively. IV. EXPERIMENTS AND RESULTS The proposed method as implemented sing MATLAB a on a desktop PC ith a modest CPU (.9GHZ), and 4GB of random access memory. In order to reflect the relationship beteen FAR and FRR, a receiver operating characteristic (ROC crve is constrcted, hich can Table I gives the corresponding eqal error rate (EER), featre extraction time (FET), featre matching time (FMT) and recognition time (RT), RT is the time for an identity, hich is defined as RT = FET FMT N s () Where N s is the nmber of template among training set in the palmprint system (sally take one image from one person as the training template ), all the palms in palmprint database are captred from 86 different people, therefore, the vale of N is 86. TABLE I. s EER, FET, FMT AND RT FOR RISF AND SR-RISF Algorithm EER(%) FET(ms) FMT(ms) RT(ms) RISF SR-RISF SR-RISF SR-RISF SR-RIS As can be seen from Fig. 7, the EERs obtained sing the idea of block ith size of 4 4, 8 8,6 6 are loer than that of only sing RISF algorithm, here the EER obtained

5 sing 8 8 is the loest, and EER vale of is higher than obtained sing RISF algorithm. By considering the reslts in table, e can get a conclsion that the recognition time of RISF algorithm is the longest, bt the bigger of block is, the smaller of recognition time it is. Hence, SR- RISF can obtain more discriative featres and redce the recognition time if e choose the reasonable sampling size. In addition, the EER of SR-RISF 8 8 is the loest and the RT is 98. ms, therefore, the optimal size of sampling size in this paper is 8 8. B. Experiment In order to verify the proposed SR-RISF method cold extract the stable featre, e performed experiments sing the PolyU and blrred-polyu palmprint databases, and making comparison ith some high-performance methods on PolyU palmprint database Palm Code [6], Fsion Code [7], Competitive Code [8], RLOC [7]. Code and Fsion Code methods, bt higher than Competitive Code and RLOC. Fig. 8(b) shos that the EER of SR-RISF is loer than those obtained sing other classical algorithm on blrred-polyu palmprint databases. A conclsion can be obtained hen compared Fig.8 (a) ith Fig.8 (b) that the EER of SR-RISF method is relatively stable both in PolyU and blrred-polyu palmprint databases. Table II lists the EER vales corresponding to Fig. 8. TABLE II. Algorithm EER VALUES CORRESPONDING TO FIG.8 EER% PolyU database Blrred-PolyU database PalmCode FsionCode Competitive Code RLOC SR-RISF FRRe(%) FRR(%) PalmCode FsionCode RLOC Competitive Code SR-RISF(8*8).5.5 a ROC crves on the PolyU database PalmCode FsionCode RLOC Competitive Code SR-RISF(8*8) b ROC crves on the Blrred-PolyU database Figre 8. ROC crves beteen SR-RISF and traditional highperformance algorithms on the different palmprint databases Fig. 8(a) is the ROC crves for SR-RISF method and classical algorithm on the PolyU palmprint databases, here the EER of SR-RISF is loer than those obtained sing Palm Recognition methods based on coding (Palm Code, Fsion Code, Competitive Code, RLOC) can better describe the palmprint textre information, and gain a higher recognition accracy. Hoever it is difficlt to represent the textre information for blrred images de to the loss of textre information. Therefore, the identification reslts of coding method are generally poor on the Blrred-PolyU palmprint database. Hoever, the proposed method (SR- RISF) can extract the relatively stable featres of blrred images, as a reslt the identification accracy and robstness of the SR-RISF is higher than those of traditional high-performance algorithms. C. Experiment With the fast development of non-contact palmprint identification system, more and more scholars are interested in it, some classical algorithms of blrred palmprint recognition are proposed, sch as DPCA [], LBP [8], LST [], DCT-PLE []. Fig. 9 shos the ROC crves of SR-RISF and some classical algorithms for blrred palmprint recognition on the Blrred-PolyU palmprint database. Table III is EER vales corresponding to Fig. 9. Fig. 9 and table IIIimply that the EER vale of SR- RISF algorithm is the loest, hich shos the effectiveness of the proposed method. For the other methods, DPCA is the algorithm based on sbspace, hich can reflect the main information of the image, hoever, this method ignores the spatial strctre information, and the identification accracy is not high. LBP is a kind of algorithm by encoding the spatial textre information, althogh it can represent the spatial strctre information of the image, bt it really depends on textre information, and hence its accracy is not high for blrred palmprint recognition. LST and DCT- PLE can extract the lo freqency coefficients as the stable featres of blrred images, hich can reflect the main information of image, bt both lack the description ability for the image spatial strctre

6 FRR(%) DPCA LBP LST DCT-PLE SR-RISF(8*8) Figre 9. ROC crves beteen SR-RISF and classical blrred palmprint recognition methods on the Blrred-PolyU palmprint database TABLE III. Algorithm EER VALUES CORRESPONDING TO FIG.9 EER% DPCA LBP LST.78 DCT-PLE.949 SR-RISF As a conseqence, the distingish ability is not high if e only se the lo freqency coefficients as featres to recognize the blrred palmprint image. On contrast, the SR- RISF algorithm proposed in this paper not only better describe the image spatial strctre bt also efficiently obtain the main information of images. V. CONCLUSIONS The proposed method in this paper cold obtain higher identification accracy and strong stability hen compared ith the traditional classical recognition methods, and meanhile the robstness is higher for noise, blrriness and illation. Hoever, the method proposed in this paper need to detere the size of the sampling area before e sed the non-overlapping sampling method, becase the size of block has a great inflence on the final recognition reslts. Therefore, based on the reslt obtained in this paper, the main do researches on ho to obtain the optimal block atomatically may be or direction for next step. ACKNOWLEDGMENT This ork as financially spported by the National Natral Science Fondation of China (No.676) and Shandong Province Higher Edcational Science and Technology Program (J4LN9). REFERENCES [] Sen Lin, Weiqi Yan, Wei W, Ting Fang, Blrred palmprint recognition based on DCT and block energy of principal line [J]. Jornal of Optoelectronics Laser,, ():-6. [] Danfeng Hong, Zhenkan Pan, Xin W, Improved Differential Box Conting ith Mlti-scale and Mlti-direction: A Ne Palmprint Recognition Method [J]. Optik- International Jornal of Light Electron Optics, 4, 5(5), [] Sen Lin, Weiqi Yan, Blrred palmprint recognition nder defocs stats [J]. Optics and Precision Engineering, :(): [4] Danfeng Hong, Wanqan Li, Jian S, Zhenkan Pan, Xin W, Robst Blrred Palmprint Recognition Based on the Fast Vese-Osher Model [J]. Commnications in Compter and Information Science, 4, 46: 8-8. [5] David Zhang, Waikin Kong, Jane Yo, Michael Wong, Online Palmprint Identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,, 5(9):4-5. [6] Wei Jia, De-Shang Hang, David Zhang, Palmprint Verification Based on Robst Line Orientation Code[J]. Pattern Recognition, 8, 4(5): 5-5. [7] Choras MKozik R. Contactless palmprint and knckle biometrics for mobile devices [J]. Pattern Analysis and Application,,5():7-85. [8] Kanhangad V, Kmar A, David Zhang, A nified frameork for contactless hand verification [J].IEEE Transactions on Information Forensics and Secrity,,6():4-7. [9] Weiqi Yan, Sye Feng, Simlation system of improved noncontact on-line palmprint recognition [J]. Acta Optica Sinica,,(7):7. [] Kang B J, Park K R. Real-time image restoration for iris recognition systems [J]. IEEE Transactions on Systems,Man,and Cybernetics, Part B:Cybernetics,7,7(6): [] Godong Wang, Jie X, Zhenkan Pan, Cnliang Li, Jinbao Yang, Blind image restoration based on normalized hyper laplacian prior term [J].Optics and Precision Engineering,, (5):4-47. [] Fang Li, Haifeng Sang, Defocsed palmprint recognition sing DPCA[C]. Proceedings of International Conference on Artificial Intelligence and Comptational Intelligence, Shanghai, China, 9:6-65. [] Chan T, Esedogl S, Aspeets of Total Variation Reglarized L Fnction Approximation.SIAM J. Appl. Math, 5, 65(5): [4] Shengnan Ye, Kaina S, Changbai Xiao, Jan Dan, Image Qality Assessment Based on Strctral Information Extraction[J]. Acta Electronica Sinica, 8,6(5) [5] Danfeng Hong, Wanqan Li, Jian S, Zhenkan Pan, Godong Wang, A Novel Hierarchical Approach for Mltispectral Palmprint Recognition [J]. Nerocompting, 5, 5:5-5. [6] Waikin Kong, David Zhang, Kamel M, Palmprint Identification Using Featre-level Fsion[J]. Pattern Recognition, 6, 9(): [7] Feng Ye, Wangmeng Zo, David Zhang, Kanqan Wang, Orientation Selection sing modified FCM for Competitive Coding- Based Palmprint Recognition[J]. Pattern Recognition, 9, 4(): [8] Zhenha Go, Lei Zhang, David Zhang, A Completed Modeling of Local Binary Pattern Operator for Textre Classification. IEEE Transactions on Image Processing,, 9(6):