Face Recognition by Fusing Binary Edge Feature and Second-order Mutual Information
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1 Face Recognton by Fusng Bnary Edge Feature and Second-order Mutual Informaton Jatao Song, Bejng Chen, We Wang, Xaobo Ren School of Electronc and Informaton Engneerng, Nngbo Unversty of Technology Nngbo, Chna Abstract Illumnaton change and expresson varaton are two man factors affectng the performance of some exstng face recognton algorthms. Edge feature based methods are robust to llumnaton change and are easy to mplement. But they don t work well for the mages wth expresson varaton. In ths paper, a novel face recognton method based on the fuson of bnary edge feature and grayscale nformaton s proposed to mprove both the llumnaton and expresson robustness. The secondorder mutual nformaton (MI s ntroduced as the smlarty metrc of grayscale face mages. Expermental results on AR dataset and Yale dataset, both wth llumnaton and expresson changes, show that the overall face recognton rate of the proposed method s better than that of some commonly used approaches, ndcatng that our method s more effectve for practcal uses. Keywords face recognton, llumnaton change, expresson change, feature fuson, bnary edge feature, grayscale feature I. INTRODUCTION uman face recognton s one of the most drect technologes for personal dentfcaton and s the hotspot of pattern recognton and computer vson. It has many potental applcatons, such as bankcard dentfcaton, access control, securty montorng and survellance. For the past two decades, many face recognton approaches have been proposed. Some of them were put nto practcal use [1-3]. Illumnaton change s one of the man factors affectng the performance of some exstng face recognton algorthms. In order to reduce the nfluence of lghtng varaton, many new face recognton algorthms have been proposed n the past several years These methods can be roughly classfed nto three categores: statstcal methods [4-6], model-based methods [7-10] and edge-based methods [11-13]. Statstcal approaches, such as Egenface method (also named PCA method [4], Fsherface method [5], Bayesan-based methods [6] and so on, are easy to mplement, but they need a number of mages for each subject as tranng mages to cover dfferent llumnaton condtons, whle ths s often mpractcal. Modelbased methods exploted the fact that the set of mages of an object n fxed pose s a convex cone n the space of mages. By usng a small number of tranng mages per subject taken from dfferent lghtng condton, a generatve model of the face can be reconstructed. For each pose, the correspondng llumnaton cone was approxmated by a low-dmensonal lnear subspace and the bass vectors can be estmated usng the reconstructed model. In order to construct the llumnaton cone, model-based methods also requre a set of tranng mages for each subject. Further more, because of the complexty of lght sources and llumnaton changes, how to compute and obtan physcally mplemented lower-dmensonal subspaces bass mages requres deep nvestgaton [7-10]. Edge reflects the dscontnuty of ntensty dstrbuton n an mage. It contans contour, structure and shape nformaton of objects n an mage. Cogntve psychologcal studes ndcated that human bengs recognze lne drawngs as quckly and almost as accurately as gray-level pctures. Further more, edge s nsenstve to llumnaton changes. Based on edge features, B.Takacs et al. proposed an algorthm usng Doubly Modfed ausdorff Dstance (MD [11], Y. Gao et al. used lne edge to represent face and defned Lne ausdorff Dstance (LD as smlarty metrc to match two lne edge mages [1]. Recently, J. Song developed a face recognton method based on bnary template matchng [13]. Expermental results show that the face recognton rates of all these edge-based methods are preferable, especally n vared lghtng cases. Comparng wth other two knds of methods, edge-based face recognton approaches extract face features drectly from testng mages, wthout usng of tranng mages, hence ths knd of methods s easer to mplement n practcal applcatons. Whle researches [1-13] show that although edge-based methods can obtan better llumnaton robustness, they don t work well on the mages wth facal expresson changes, especally wth exaggerated expresson changes, such as scream, whch lmts ts applcaton. In ths paper, a novel face recognton method based on the fuson of bnary edge and grayscale nformaton s proposed to mprove both the llumnaton robustness and expresson robustness. Expermental results on Yale face database [14] and AR database [15] show that our method s effectve for mages wth both llumnaton changes and facal expresson varatons. II. TE PROPOSED METOD A. Image Smlarty Metrc Based on Sngle Feature 1 Edge-based smlarty metrc bnary edge dstance Let B and BTeI be two bnary edge mages to be matched, N and N TeI be the number of foreground pxels n B and BTeI respectvely, and Noverlap be the number of common foreground pxels n B and BTeI. The smlarty /08 /$ IEEE CIS 008
2 between these two bnary edge mages, namely Bnary Edge Dstance (BED, s defned as [13]: N overlap BED( B, N + NTeI (1 Obvously, the value of BED ( B, s between 0 and 1. If two mages are the same, N overlap N NTeI, so BED ( B, 1. If there s no overlapped foreground pxels, then N overlap 0 and BED ( B, 0. Thus, the greater the value of BED ( B,, the more smlar the two matched mages. Grayscale-based smlarty metrc second-order mutaual nformaton dstance Mutual nformaton (MI s a basc concept of Informaton Theory. It measures the statstcal correlaton of two random varables. It also reflects the amount of nformaton of one varable carred by another varable. The mutual nformaton of two mages A and B, denoted wth MI(A,, can be defned n terms of the frst-order entropes of these two mages, denoted wth (A and (, and ther jont entropy (A,,.e MI( A, ( A + ( ( A, ( The frst-order entropy (A s often calculated usng the grayscale probablty dstrbuton functon p (a of each pxel n the mage,.e., ( A p( alog p( a a A (3 a But ths may lead to the overestmate of the true entropy of an mage, because t s based on the assumpton that the ntensty values of each pxels n an mage are probablty-ndependent, whch s often not true n practce. To address ths problem, Rueckert et al.[16] proposed a new smlarty metrc based on second-order entropy and second-order mutual nformaton (MI. The second-order entropy of mage A, denoted wth (A, can be defned as ( A p(, j log p(, j (4 j Grayscale target mage group where p(, j denotes the jont probablty that a pxel has ntensty value of whle ts neghborng pxel has ntensty value of j. The value of p(, j can be estmated from D jont hstogram. In order to compute MI, the second-order jont entropy should be frstly calculated, t can be defned as follows: (, A p (,,,log(,,, j kl p j kl (5 j k l where p(, j, k, l denotes the jont probablty that a pxel and ts neghbor n mage A have ntensty values of and j, respectvely, whle the correspondng pxel and ts neghborng pxel n mage B have ntensty values of k and l, respectvely. p(, j, k, l can be estmated from 4D jont hstogram. Lke frst-order MI, the second-order mutual nformaton, denoted wth MI ( A,, can be calculated wth MI B ( B A, ( A + ( ( A, (6 It can be further normalzed as ( A + ( NMID ( A, (7 ( A, In ths paper, the normalzed second-order mutual nformaton dstance NMID ( A, defned n (7 s used as the smlarty metrc of two mages. Obvously, t s based on the grayscale feature of mages. The ntensty value j of the neghborng pxel n (4 and (5 s estmated usng the mean value of four-neghborng pxels [17]. B. Face Recognton Based on the Fuson of Bnary Edge and Grayscale Features There exst three knds of nformaton fuson methods,.e. fuson at feature level, fuson at matchng score level and fuson at decson level. In ths paper, the thrd knd nformaton fuson method s adopted for our face recognton purpose. The detaled feature fuson scheme s llustrated n Fg 1. Let TeI be grayscale mage to be tested, ( 1,,, n be target grayscale mages, n s the number Input mage Compute NMID Extract Bnary edge feature Compute N_N MID Compute BED Compute N BED ρ ( 1 ρ S Decson Recognton Result Bnary edge target mage group Fgure 1.Face recognton based on the fuson of edge and grayscale features
3 of target mages. Let BTeI and B be the correspondng bnary edge mage of TeI and, respectvely, NMID ( TeI, be the MI dstances between TeI and calculated usng (7, BED( B be the BED dstances between BTeI and B calculated usng (1,. N NMID( TeI, and N _ BED( B be the _ mproved verson of NMID ( TeI, and BED ( B, respectvely, wth ther values normalzed to [0,1]. Then, The smlarty metrc by fusng the bnary edge and grayscale features, denoted wth S ( TeI,, s defned as follows: S( TeI, ρ N _ NMID ( TeI, + (1 ρ N _ BED( B ( 1,,, n (8 where ρ s an adaptve weght factor defned as follows: 1 ρ f ( ω (9 5( ω ω0 1+ e where ω ω ω 1 1 ( ω 1 + ω BED( B BED( B, B ( BED( B BED( B, B 1 n BED( B 1 ( BED( B 1 1 n BEDB (, B ( 1 NMID ( TeI, NMID (, ω ( NMID ( TeI, NMID (, 1 n NMID ( TeI, ( NMID ( TeI, 1 n NMID (, ( ω mean( ω 0 1 n where mean( s a mean value functon. The matchng result of tested mage TeI, denoted wth R, s determned by the followng rule: R arg ( S( TeI, (10 1 n III. EXPERIMENTAL RESULTS AND ANALYSIS The AR face database [14] and the Yale face database [15] are used for our face recognton experments. For each subject, only one mage s selected as the target mage. All the mages ncludng those n the target group were manually normalzed n geometry and grayscale usng the method descrbed n [18]. The dstance between two rses s set to 60 pxels and the resoluton of the normalzed mages s The bnary edge features are extracted usng the LAT algorthm proposed n [19]. Some normalzed face mages and ther correspondng bnary maps are shown n Fg. and Fg. 3. The AR mage set used ncludes all 931 mages stored on the frst four CD- ROMs of the AR database. These mages were taken from 133 subjects wth three lghtng varatons (left lght on, rght lght on, and all sde lghts on wth neutral facal expresson, and four facal expresson varatons (neutral, smle, scream and anger under normal lghtng condton. At testng stage, 133 mages wth neutral expresson and normal llumnaton condton are selected as target mages. The rest 798 mages are dvded nto 6 groups. Each group was tested ndvdually. The Yale mage set used conssts of the 135 mages of 11 subjects wth dfferent llumnaton and dfferent expresson condtons. All these mages are dvded nto 9 groups, correspondng to dfferent lghtng condtons and facal expressons. Each group has 15 mages. The group wth normal llumnaton and neutral expresson s chosen as target group. The other eght groups, correspondng to three varatons n lghtng condton (center lght, left lght, rght lght and fve facal expresson varatons (happy, sad, sleepy, surprsed, wnk, are selected as test mages. TABLE Ⅰ and TABLE Ⅱ show the face recognton results of fve dfferent methods on the AR mages and Yale mages, respectvely. These approaches are: (1 the BED-based method, ( the NMID -based method, (3 the proposed feature fuson based method, (4 the MD method and (5 the PCA method. From TABLE Ⅰ and TABLE Ⅱ, we can conclude that: a For mages wth lghtng changes, the BED based method acheves better average face recognton rates than another bnary edge feature based MD method on both the AR subset and Yale subset used. Also, the NMID based method obtan better performance than another grayscale feature based PCA method on these mages. b For mages wth expresson changes, on both the AR subset and Yale subset, the edge based methods, ncludng MD approaches and BED based method, are not as good as the grayscale based methods, such as the NMID based method and the tradtonal PCA method. Ths s concde wth the results reported n [1] and [13]. c The proposed feature fuson based method acheves an overall face recognton rate of 87.7% on AR mages and that of 84.17% on Yale mages. Both are better than the face recognton results obtaned usng other methods. Ths ndcates that by combng the edge feature and grayscale feature together, the proposed method can acheve both better lghtng robustness and better expresson robustness, leadng t more applcable n practcal uses. IV. CONCLUSION The llumnaton change and expresson varaton are two
4 Fgure.Example of normalzed face mage (top: AR mages, bottom: Yale mages Fgure 3.Correspondng bnary edge mages of Fg. (top: AR mages, bottom:yale mages Methods TABLE I. FACE RECOGNITION RATES OF DIFFERENT METODS ON AR IMAGES (% Dfferent lghtng Dfferent expresson left lght on rght lght on all sde lghts on average Smle Anger Scream average Overall BED NMID Proposed method MD PCA TABLE II. FACE RECOGNITION RATES OF DEFFERENT METODS ON YALE IMAGES (% Methods Dfferent lghtng Dfferent expresson center-lght left-lght Rght-lght average happy sad sleepy surprsed wnk average Overall BED NMID Proposed method MD PCA urgent problems to be solved for face recognton nowadays. Edge feature based methods have good llumnaton robustness, but they are less robust to expresson change than grayscale feature based methods. Consderng the lmtatons of sngle edge feature or grayscale feature based methods, a novel method by fusng edge feature and grayscale feature s presented n ths paper. Expermental results on both AR mages and Yale mages show that the proposed method
5 acheves better overall face recognton rates than other commonly used edge based or grayscale based method The future work s to develop more effectve feature fuson scheme to further mprove the face recognton performance. ACKNOWLEDGMENT The work descrbed n ths paper was partally supported by a grant from Nngbo Natural Scence Foundaton (006A and 007A and two grants from Zhejang Provncal Natural Scence Foundaton of Chna (Y10539 and The author also gratefully acknowledges the support of K. C. Wong Educaton, ong Kong. REFERENCES [1] R. Chellappa, C. L. Wlson, and S. Srohey, uman and machne recognton of faces: A survey, Proceedngs of the IEEE, vol. 83, no. 5, pp , [] W. Y. Zhao, R. Chellappa, A. Rosenfeld, and J. Phllps, Face recognton: A lterature Survey, ACM computng Survey, vol. 35, no. 4, pp , 003. [3] P. J. Phllps, P. Grother, R. J. Mcheals, D. M. Blackburn, E. Tabass, M. Bone, Face recognton vendor test 00: Evaluaton report,. Techncal Report, NISTIR 6965, Gathersburg. Natonal Insttute of Standards and Technology, 003. [4] M.Turk, A.Pentland, Egenfaces for recognton. Journal of Cogntve, Neuroscence, vol. 3, no. 1, pp , [5] P. N. Belhumeur, J. P. espanha, and D. J. Kregman, Egenfaces vs. Fsherfaces: Recognton usng class specfc lnear projecton, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 19, no. 7, pp , [6] B. Moghaddam, T. Jebara, and A. Pentland, Bayesan face recognton, Pattern Recognton, vol. 33, no.11, pp , 000. [7] P. Belhumeur, D. Kregman, What s the set of mages of an object under all possble lghtng condtons, Internatonal Journal of Computer Vson, vol. 8, no. 3, pp , [8] R. Basr, D. Jacobs, Lambertan reflectance and lnear subspaces, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 5, no., pp. :18-33, 003. [9] K. Lee, J. o, and D. Kregman, Acqurng lnear subspaces for face recognton under varable lghtng, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 7, no. 5, pp , 005. [10] L. Zhang, D. Samaras, Face recognton from a sngle tranng mage under arbtray unknown lghtng usng sphercal harmoncs, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 8, no. 3, pp , 006. [11] B. Takacs, Comparng face mages usng the modfed ausdorff Dstance, Pattern Recogn- ton, vol. 31, no. 1, pp ,1998. [1] Y. Gao, M.K..Leung, Face recognton usng lne edge map, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 4, no. 6, pp , 00. [13] J. Song, B. Chen, and Z. Ch, X. Qu, and W. Wang, Face recognton based on bnary template matchng, In Proceedngs of the Thrd Internatonal Conference on Intellgent Computng(ICIC007, Sprnger Publshers, Berln/ edelberg, pp , August, 007. [14] A.M. Martnez and R. Benavente, The AR Face Database, CVC Techncal Report #4, June [15] P. N. Belhumeur, Yale face database, [16] D. Rueckert, M. J. Clarkson, D. ll, and D. awkes, Non-rgd regstraton usng hgher- order mutual nformaton, In Proceedngs of SPIE, Medcal Imagng: Image Processng, vol. 3979, pp , 000. [17] B. Chen, J. L, and G. Chen, Study of medcal mage regstraton based on second-order mutual nformaton, In Proceedngs of the 1st IEEE Internatonal Conference on Bonformatcs and Bomedcal Engneerng(ICBBE007,IEEE Xplore, pp , 007. [18] Z. Ban, X. Zhang, Pattern Recognton, nd Edn. Tsnghua Unversty Press, Bejng, 000 (n Chnese. [19] S. Y. Lee, Y. K. am, R. Park, Recognton of human front faces usng knowledge-based feature extracton and neuro-fuzzy algorthm, Pattern Recognton, vol. 9, no. 11, pp ,1996.
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