Palmprint Recognition Using Directional Representation and Compresses Sensing

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1 Research Journal of Appled Scences, Engneerng and echnology 4(22): , 2012 ISSN: Maxwell Scentfc Organzaton, 2012 Submtted: March 31, 2012 Accepted: Aprl 30, 2012 Publshed: November 15, 2012 Palmprnt Recognton Usng Drectonal Representaton and Compresses Sensng 1 Hengjan L, 1 Lanha Wang and 2 Zutao Zhang 1 Shandong Provncal Key Laboratory of computer Network, Shandong Computer Scence Center, Jnan , Chna 2 School of Mechancal Engneerng, Southwest Jaotong Unversty, Chengdu , Chna Abstract: In ths study, based on drectonal representaton for palmprnt mages and compressed sensng, we propose a novel approach for palmprnt recognton. Frstly, the drectonal representaton for appearance based approaches s obtaned by the ansotropy flter to effcently capture the man palmprnt mage characters. Compared wth the tradtonal Gabor representatons, the new representatons s robust to drastc llumnaton changes and preserves mportant dscrmnatve nformaton for classfcaton. hen, n order to mprove the robustness of palmprnt dentfcaton, the compressed sensng s used to dstngush dfferent palms from dfferent hands. As a result, the palmprnt recognton performance of representatve appearance based approaches can be mproved. Expermental results on the PolyU palprnt database show that the proposed algorthm has better performance and wth good robustness. Keywords: Compressed sensng, drectonal representaton, mage processng, palmprnt recognton INRODUCION Wth the development of nformaton and networked socety, the applcaton of bometrc recognton systems wll be wlder and brngs much challenge to the researchers. Recently years, palmprnt recognton has drawn wde attenton from researchers for ts specal advantages such as stable lne features, rch texture features, low-resoluton magng, low-cost capturng devces, easy self postonng and user-frendly nterface (Zhang et al., 2003). At present, varous palmprnt recognton algorthms have been proposed to mprove the recognton performance (Kong et al., 2009). In bometrc recognton research areas, the research on appearance based approaches were frstly proposed to be used n face recognton and had been attracted a lot of researchers (Belhumeur et al., 1997). Also, they were also successfully appled to palmprnt recognton. For example, Based on Prncpal Components Analyss (PCA) and Lnear Dscrmnant Analyss (LDA) and ther versons, has been effcently exacted the palmprnt features (Lu et al., 2003; Wu et al., 2003). Gabor wavelets are extensvely employed to extract face feature for bometrc recognton and have obtaned better performance than the orgnal mage samples for ther smlar characterstcs to those of human vsual system (Lee, 1996). However, n one way, the Gabor wavelet representaton has two unavodable drawbacks. Frst, t s computatonally very complex. Second, memory requrements for storng Gabor features are very hgh (Shen and Ba, 2006). In the other ways, dfferent from other bometrc trats, such as the projecton features n face mages and the texture nformaton n rs mages (Daugman, 2004), the orentaton nformaton n palmprnt s the fundamental character and the Gabor representatons cannot express the lne orentaton very well. However, mult-orentaton based approaches are deemed to have the best performance n palmprnt recognton feld (Yue et al., 2009; L et al., 2010), because orentaton feature contans more dscrmnatve nformaton than other features and s nsenstve to llumnaton changes. he smplest classfcaton scheme s a nearest neghbor classfer to dstngush dfferent bometrc mage trats (Hu et al., 2008). However, t does not work well under varyng lghtng condtons. Based on a sparse representaton computed by l1-mnmzaton, a general superor performance classfcaton algorthm for bometrc recognton feld (Wrght et al., 2009; Wrght et al., 2010). In ths study, to mprove the robustness of extracted features, therefore, the drectonal representatons of palmprnt mages usng an ansotropy flter s proposed to mprove the drectonal representatons of palmprnt mages. hen, feature extracton and dmenson reducton usng PCA and classfcaton usng compressed sensng. At last, expermental results on PolyU Palmprnt Database Correspondng Author: Hengjan L, Shandong Provncal Key Laboratory of computer Network, Shandong Computer Scence Center, Jnan , Chna 4724

2 (a) (b) Fg. 1: Appearance of ansotropc flter are gven to demonstrate the effectveness of proposed approach. MEHODOLOGY he drectonal representaton for palmprnt mages: he Ansotropc Flter (AF) s frstly used n buldng over-complete dctonary to obtan sparse representaton by the dea of effcently approxmatng contour-lke sngulartes n 2-D mages. he AF s a smooth low resoluton functon n the drecton of the contour and behaves lke a wavelet n the orthogonal (sngular) drecton. hat s, the AF s bult on Gaussan functons along one drecton and on second dervatve of Gaussan functons n the orthogonal drecton. he structure of AF s very specal for capturng the orentaton of palmprnt mage (L and Wang, 2012). he AF has the followng general form: (, ) = ( 4 2) exp( ( + )) Guv u u v (1) where, (u, v) s, n ths case, the plane coordnate and can be obtaned n the followng way: u v = 1/ α 0 cosθ 0 1/ β snθ snθ x x0 cosθ y y0 (2) where, [x 0, y 0 ] s the center of the flter, the rotaton 2, to locally orent the flter along palm contours and " and $ are to adapt to contour type. he choce of the Gaussan envelope s motvated by the optmal jont spatal and frequency localzaton of ths kernel and by the presence of second dervatve-lke flterng n the early stages of the human vsual system. It s also motvated by the presence of second dervatve-lke flterng n the early stages of the human vsual system. Usually, $>" s set to better obtan the lne orentaton of palmprnts. A 3D vsualzaton of an AF can be seen n Fg. 1. he orentaton of a pxel can be calculated by the formula: ( p ) j = arg mn I * G θ dxdy p (3) (c) Fg. 2: Plmprnt mages and ther drectonal representaton. (a) and (c) come from the same palmprnt, but were captured n dfferent llumnaton condtons. (b) and (d) are ther correspondng drectonal representons where, j s called the drectonal ndex, * represent convoluton operaton. he orentatons of the twelve flters, 2 p are p/b12, where p = 0, 1, 2, 11. By ths means, the drectons of every pxels can be computed f the center of AF moves through out an mage pxel by pxel. If an mage s m n, the drectonal representatons of an mage can be obtaned by ther ndex values of drectons. Fgure 2 shows three palmprnt mages and ther drectonal representatons, t correspondng parameters are (", $, x 0, y 0 ) = (5, 23, 12, 12). Among them, Fg. 2a and c come from the same palmprnt, but were captured n dfferent llumnaton condtons. Although the llumnaton condtons changed drastcally, however, ther drectonal representatons are stll very smlar (Fg. 2b and d). From ths example, t can be concluded that the proposed pamprnt drectonal representaton s also robust for the change of llumnaton. he proposed algorthm: Usually, Regon of Interest (ROI) from the orgnal palmprnt mages are extracted to algn dfferent palmprnt mages for matchng. Compared wth the heaven computatonal burden n the Gabor representatons, here we used the drectonal palmprnt representatons nstead, whch can extract orentaton and lne feature effectvely. At the next stage, the PCA s used to extract the feature and reduce dmenson of palmprnt mages. At last, the compressed sensng s used to classfy the palms from dfferent hands, whch s rubost to mperfect mage captured and preprocessed. ROI parts of palmprnt mage: Once the palmprnt s captured, t s processed to get the Regon of Interest (d) 4725

3 transformaton W, the scatter of the transformed feature vectors {y 1, y 2, y N } s W S W. In PCA, the projecton W opt s chosen to maxmze the determnant of the total scatter of the projected samples,.e.: (a) Fg. 3: (a) he determnaton of ROI, (b) A cropped ROI mage of the palmprnt mage n, (a) (ROI), whch s a area. he ROI parts are employed for feature extracton and dentty recognton. hs process wll also reduce, to some extent, the effect of rotaton and translaton of the hand va defnng a coordnate system, whch can be found n Zhang et al., (2003) for the detaled. Fgure 3 llustrates a ROI mage cropped from the orgnal palmprnt mage. he ROI parts contan the most part of nformaton and are used n the followng recognton stage. Prncple component analyss: PCA has been wdely used for dmensonalty reducton and as lnear feature extracton n computer vson. PCA, also known as Karhunen-Loeve methods, computes the bass of a pace whch s a space whch s represented by ts tranng vectors yelds projecton drectons that maxmze the total scatter across all classes. hese bass vectors, actually egenvectors, computed by PCA are n the drecton of the largest varance of the tranng vectors. he ntrnsc dmensonalty of egenvectors s smaller than the orgnal mage data space. he economcal data representatons of PCA show that t can performs well n varous recognton tasks. And PCA s one of the most successful technques that have been used n mage recognton(brunell and Poggo,1993). More formally, let us consder a set of N sample mages {x 1, x 2,..., x N } takng values n an n-dmensonal mage space and assume that each mage belongs to one of c classes {X 1, X 2,, X c }. Let us also consder a lnear transformaton mappng the orgnal n-dmensonal mage space nto m-dmensonal feature space, where m<n. he new feature vectors y k, m are defned by the followng lnear transformaton: y = W k =12,,... N k xk (b) (4) where, W, n m s a matrx wth orthonormal columns. If the total scatter matrx S s defned as: N ( )( ) S = xk µ xk µ k = 1 (5) where, n s the number of sample mages and µ =, m s the mean mage of all samples, then after applyng the lnear [ 1 2 m] W = arg max W S W = w, w,... w opt W (6) where, {w = 1, 2, m} s the set of n-dmensonal egenvectors of S correspondng to the m largest egenvalues. hese egenvectors have the same dmenson as the orgnal mages. Compressed sensng for classwcaton: Sparse representaton, whch are representatons that account for most or all nformaton of a sgnal wth a lnear combnaton of a small number of elementary sgnals, has proven to be an extremely powerful tool for representng natural mages. Fndng a representaton wth a small number of sgnfcant coeffcents can be solved as the followng optmzng problem: x$ = arg mn x 0 0 subject to Dx = y (7) where,. 0 denotes the l 0 -norm, whch counts the number of nonzero entres n a vector. Seekng the sparsest soluton to Dx = y s a NP problem. he theory of sparse representaton and compressed sensng reveals that f the soluton x 0 sought s sparse enough, the soluton of the l 0 -mnmzaton problem s equal to the soluton to the l 1 - mnmzaton problem. Gven suffcent tranng palmprnt samples of the -th m n object hand class, D = [ d d d n ], 1,, 2,...,,, a test palmprnt sample y, m from the same hand wll approxmately le n the lnear span of the tranng palmprnt samples assocated wth object. y = D x for some coeffcent n vector x. herefore, gven a new test palmprnt sample feature y from one of the classes n the tranng feature set, we frst compute ts sparse representaton va bass pursut. Usually, the small nonzero entres n the estmaton assocated wth the columns of D from a sngle object class I and can easly assgn the test palmprnt feature y to that class. Based on the pror sparse representaton of palmprnt mages, one can treat the test feature can be treated as a lnear combnaton of all tranng features of each object. And, one can dentfy the rght class from multple possble classes. It can be computed as follows: For each class, let 8 : n 6 n be the characterstc functon whch selects the coeffcents assocated wth the -th class, one can obtan the approxmate representaton y$ ( $ = Dλ x 1 ) for the gven test sample y. We then classfy y based on the approxmatons by assgnng t to the object class that mnmzes the resdual between y and $y : r( y) y D ( x ) (Wrght et al., 2009). he = λ $

4 Fg. 4: he flowchart of the proposed palmprnt recognton emprcal complexty of the commonly used l 1 -regularzed sparse codng methods (Km et al., 2007; Berg and Fredlander, 2008). Palmprnt recognton usng drectonal representaton and compresses sensng: From the above dscusson, based on drectonal representatons and compressed sensng, we proposed a lght computatonal burden and robustness palmprnt recognton. As llustrated n Fg. 4, the recognton system can be brefly summarzed as follows: Step 1: For convenence durng n the feature extractng, the gaps between the fngers as reference ponts to determne a coordnate system s used to extract the regon part of a palmprnt mage. Step 2: he drectonal representatons of the preprocessed palmprnt mage are obtaned va a bank of ansotropc flter wth twelve orentatons on the ROI part of palmprnt mages. Step 3: he PCA s employed to reduce dmenson and extract the feature of the drectonal representatons of palmprnt mages effcently. PCA uses the egenvectors of the covarance matrx. Step 4: he egenvectors as the feature s calculated by compressed sensng and employed to measure the smlarty of two palmprnts from dfferent hands. EXPERIMENAL RESULS AND ANALYSIS In PolyU Palmprnt Database, there are 600 gray scale mages captured from 100 dfferent palms by a CCD-based devce ( www. comp. polyu. edu.hk/ bometrcs.). Sx samples from each palm are collected n two sessons: the frst three samples were captured n the frst sesson and the other three were captured n the second sesson. he average tme nterval between these two sessons was two months. he sze of all the mages n the database was wth a resoluton of 75 dp. In our experments, a central part ( ) of each Recognton rate (%) Feature dmenson Drs + CS Drs + NN Gabor + CS Gabor + NN PCA + CS PCA + NN Fg. 5: Recognton performance of dfferent approaches wth varyng feature dmenson mage s extracted for further processng. he results have been generated on a PC wth an Intel Pentum 2 processor (2.66 GHz) and 3 GB RAM confgured wth Mcrosoft Wndows 7 professonal operatng system and Matlab (R2010a). In our experments, a hghly effcent algorthm sutable for large scale applcatons, known as the Spectral Projected Gradent (SPGL1) algorthm (Berg and Fredlander, 2008), s employed to solve the BP and BPDN problems. In the mplementaton of Gabor flters, the parameters are set as k max = B/2, F = 2B, f = 2, u = {0, 1,...11}, v = {0, 1, 2}. he feature vector of the nput palmprnt s matched aganst all the stored templates and the most smlar one s obtaned as the matchng result. he frst three samples of each palm are selected for tranng and the remanng three samples are used for testng. Followng these schemes, we have calculated recognton rates wth the dmensons rangng from 5 to 145. he expermental results are shown n Fg. 5. As we can see from ths Fg. 5, the correct recognton rate ncreases wth the ncreasng of the dmenson of features and t surpasses 90% when the dmenson equals to or exceeds 25. he Fg. 5 also suggests that the recognton rate of our proposed method (Ours) has better performance than all the other approaches under the same condton. From the Fg. 5, the CS classfcaton methods s better than NN (Nearest Neghbour) for the same features. For the feature dmenson s lower than 45, the

5 able 1: Runnng tme wth dfferent approaches (feature dmensons: 40) Algorthms PCA+NN PCA+CS Gabor+NN Gabor+CS Drs+NN Drs+CS Recognton rate 82.33% 88.00% 93.67% 95.67% 95.67% 97.00% me consumed (sec) drectonal representatons based approaches has better performance than Gabor methods. When the feature dmenson s lager than 45, the performance of drectonal representaton and Gabor are nearly the same. able 1 llustrates the computng tme of the proposed approach and other approaches. Form the able 1, the computatonal runnng tme of the proposed approach for feature extracton and classfcaton s shorter than the Gabor based approaches. he performance of approaches based on Drectonal Representatons s a lttle better than the Gabor-based. However, the runnng tme of Gabor based palmprnt recognton algorthms s 1.5 tmes of that Drectonal Representatons based algorthms. CONCLUSION In ths study, a novel approach for palmprnt recognton s proposed. Frstly, a new drectonal representaton for appearance based approach usng the ansotropy flter for palmprnt recognton s presented. Compared wth orgnal representaton, the desgned drectonal representaton contans stronger dscrmnatve nformaton and s nsenstve to llumnaton changes. hen, appearance based approaches, such as PCA, s used to extract the palmprnt features. Fnally, a compressed sensng classfcaton s employed to dstngush dfferent palms from dfferent hands. he expermental results clearly demonstrated that the proposed algorthm has much better performance than Gabor-based algorthm and the tradtonal NN classfer. ACKNOWDGMEN hs study s supported by grants by Natonal Natural Scence Foundaton of Chna Grant No , by the Shandong Provnce Outstandng Research Award Fund for Young Scentsts of Chna Grant No. BS2011DX03 & BS2010DX029 and by the Shandong Natural Scence Foundaton Grant No. ZR2011FQ030. REFERENCES Belhumeur, P.N., J. Hespanha and D.J. Kregman, Egen faces vs. fsher faces: Recognton usng class specfc lnear projecton. IEEE. Pattern Anal., 19(7): Berg, E. and M.P. Fredlander, Probng the pare to fronter for bass pursut solutons. SIAM J. Sc. Comp., 31(2): Brunell, R. and. Poggo, Face recognton: Features vs. templates. IEEE. Pattern Anal., 15(10): Daugman, J.G., How rs recognton works. IEEE. Crcuts Syst. Vdeo echnol., 14(1): He, X., S. Yan, Y. Hu, P. Nyog and H.J. Zhang, Face recognton usng Laplacanfaces. IEEE. Pattern Anal., 27(3): Hu, Q., D. Yu and Z. Xe, Neghborhood classfers. Expert Syst. Appl., 34: Km, S.J., K. Koh, M. Lustg, S. Boyd and D. Gornevsky, An nteror-pont method for large-scale l1-regularzed least squares. IEEE J. Select. op. Sgnal Proc., 1(4): Kong, A., D. Zhang and M. Kamel, A survey of palmprnt recognton. Pattern Recogn., 42(7): Lee,.S., Image representaton usng 2D gabor wavelets. IEEE. Pattern Anal., 18(10): L, H., J. Zhang and Z. Zhang, Generatng cancelable palmprnt templates va coupled nonlnear dynamc flters and multple orentaton palm codes. Inf. Sc. 180(20): L, H.J. and L.H. Wang, Chaos-based cancelable palmprnt authentcaton system. Proceda Eng., 29: Lu, G.M., D. Zhang and K.Q. Wang, Palmprnt recognton usng egenpalms features. Pattern Recogn. Lett., 24(9-10): Shen, L.L. and L. Ba, A revew on Gabor wavelets for face recognton. Pattern Anal. Appl., 9: Wrght, J., A.Y. Yang, A. Ganesh, S.S. Sastry and Y. Ma, Robust face recognton va sparse representaton. IEEE. Pattern Anal., 31: Wrght, J., J.Y.M. Maral, J. Sapro, G. Huang and.s. Shucheng, Sparse representaton for computer vson and pattern recognton. Proc. IEEE, 98(6): Wu, X.Q., D. Zhang and K.Q. Wang, Fsher palms based palm prnt recognton. Pattern Recogn. Lett., 24(15): Yue, F., W.M. Zuo and D. Zhang, FCM-based orentaton selecton for compettve code-based palm prnt recognton. Pattern Recogn., 42(11): Zhang, D., A. Kong, J. You and M. Wong, Onlne palm prnt dentfcaton. IEEE. Pattern Anal., 25(9):