Infrared face recognition using texture descriptors

Transcription

1 Infrared face recognton usng texture descrptors Moulay A. Akhlouf*, Abdelhakm Bendada Computer Vson and Systems Laboratory, Laval Unversty, Quebec, QC, Canada G1V0A6 ABSTRACT Face recognton s an area of computer vson that has attracted a lot of nterest from the research communty. A growng demand for robust face recognton software n securty applcatons has drven the development of nterestng approaches n ths feld. A large quantty of research n face recognton deals wth vsble face mages. In the vsble spectrum the llumnaton and face expressons changes represent a sgnfcant challenge for the recognton system. To avod these problems, researchers proposed recently the use of 3D and nfrared magng for face recognton. In ths work, we ntroduce a new framework for nfrared face recognton usng texture descrptors. Ths framework explots lnear and non lnear dmensonalty reducton technques for face learnng and recognton n the texture space. Actve and passve nfrared magng modaltes are used and comparson wth vsble face recognton s performed. Two multspectral face recognton databases were used n our experments: Equnox Database (Vsble, SWIR, MWIR, LWIR) and Laval Unversty Multspectral Database (Vsble, NIR, MWIR, LWIR). The obtaned results show hgh ncrease n recognton performance when texture descrptors lke LBP (Local Bnary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtaned n the short wave nfrared spectrum (SWIR) usng non lnear dmensonalty reducton technques. Keywords: Face recognton, nfrared magng, bometrcs, textures. 1. INTRODUCTION Face recognton s an area of computer vson that has attracted a lot of nterest from the research communty. A growng demand for robust face recognton software n securty applcatons has drven the development of nterestng approaches n ths feld. A large quantty of research n face recognton deals wth vsble face mages [1], [2]. In the vsble spectrum the llumnaton changes represent a sgnfcant challenge for the recognton system. Illumnaton change ntroduces a lot of errors durng the recognton phase. Another challenge for face recognton n the vsble spectrum nvolves the changes n facal expressons. Facal expresson can lead to a poor performance of the face recognton system n vsble mages. To avod these problems, researchers propose the use of 3D face recognton [3] and nfrared face recognton [4], [5]. Infrared face recognton s a growng area of research. Many of the technques used n nfrared face recognton are nspred from ther vsble counterparts. Known technques used n vsble face mage recognton are also used wth nfrared mages, lke Egenfaces or Fsherfaces [4], [5]. More recently n [6], [7] and [8] physologcal nformaton extracted from hgh temperature regons n thermal face mages were used n nfrared face recognton. The vast maorty of nfrared face recognton technques are based on lnear approaches used n vsble face recognton. Recent work has been conducted usng non lnear dmensonalty reducton [9] and Bayesan technques for nfrared face recognton wth promsng results [10]. Texture analyss have been wdely studed n the lterature. Many methods have been proposed n order to handle machne vson problems where texture features serve as a cue for classfcaton, segmentaton and recognton. Varous statstcal descrptors have been proposed for the measure of mage textures [11], [12]. Snce ts ntroducton, Local Bnary Pattern (LBP) texture descrptor has been successfully used n texture classfcaton [13]. Many work was done usng LBP for vsble spectrum face recognton [14]-[18]. *akhlouf@gel.ulaval.ca; phone ; fax ; vson.gel.ulaval.ca Thermosense XXXII, edted by Ralph B. Dnwdde, Morteza Safa, Proc. of SPIE Vol. 7661, SPIE CCC code: X/10/$18 do: / Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

2 In ths work we ntroduce the use of LBP lke texture descrptors for effcent multspectral face recognton. The proposed descrptors wll be used n actve and passve nfrared spectrums face recognton. Local Bnary Pattern (LBP) and Local Ternary Pattern (LTP) descrptors are used. Also a smple dfferental LTP descrptor (DLTP) s ntroduced. The proposed texture space s less senstve to nose, llumnaton change and facal expressons. These characterstcs make t a good canddate for effcent multspectral face recognton. Lnear and non lnear dmensonalty reducton technques are used for performance evaluaton of multspectral face recognton n the texture space Tests were conducted usng two multspectral nfrared face databases: Equnox multmodal face database [19] and a new nfrared multspectral face database we have developed recently n order to evaluate nfrared face recognton technques n a close to real world stuatons. Ths new database s brefly ntroduced n the followng secton. 2. INFRARED FACE DATABASES In ths work we used the popular Equnox database and a new multspectral database we developed for multspectral face recognton tests. The Equnox database [19] s a large collecton of face mages. These mages were acqured usng a specal setup formed by vsble and nfrared cameras and a controlled lghtng system (frontal, left and rght lghts). The followng modaltes are avalable: Vsble ( μm); Short-wave (SWIR, μm); Md-wave nfrared (MWIR, 3-5 μm); Long-wave nfrared (LWIR, 8-12 μm). The mage acquston was conducted under controlled condtons. Multple facal expressons, llumnaton changes and facal mages wth and wthout eyeglasses are avalable. Also, a new multspectral face database [9] was developed n order to evaluate the performance of nfrared face recognton technques and compare t wth the vsble spectrum face recognton. Ths database has the followng spectrums (Fgure 1): Vsble ( μm); Near nfrared (NIR, μm); Md-wave nfrared (MWIR, 3-5 μm); Long-wave nfrared (LWIR, 8-12 μm). The mages have a hgher resoluton than equnox (640x480 for vsble and NIR mages and 640x512 for MWIR mages). Image acquston was conducted n a less controlled envronment compared to Equnox. The database contans multple facal expressons, changes over tme, metabolc varatons (after temperature change due to exercse) and presence of eyeglasses. Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

3 (a) (b) (c) (d) Fgure 1. Example mages wth dfferent expressons from Laval Unversty multspectral face database: (a) vsble happy, (b) near nfrared angry wth eyeglasses, (c) md-wave nfrared wth glasses and a smle and (d) long-wave nfrared neutral. 3. TEXTURE DESCRIPTORS 3.1 Local Bnary Pattern (LBP) The local bnary pattern (LBP) texture analyss operator was ntroduced n [13]. It s a gray-scale nvarant texture measure computed from the analyss of a 3x3 local neghbourhood over a central pxel. The LBP s based on a bnary code descrbng the local texture pattern. Ths code s bult by thresholdng a local neghbourhood by the gray value of ts center. The eght neghbours are labelled usng a bnary code {0, 1} obtaned by comparng ther values to the central pxel value. If the tested gray value s below the gray value of the central pxel, then t s labelled 0, otherwse t s assgned the value 1: 0 f P < P 1 otherwse ' 0 = P (1) ' P s the obtaned bnary code, P s the orgnal pxel value at poston and P0 s the central pxel value. Wth ths technque there s 256 (2 8 ) possble patterns (or texture unts).the obtaned value s then multpled by weghts gven to the correspondng pxels. The weght s gven by the value 2-1. Summng the obtaned values gves the measure of the LBP (Fgure 2): 8 ' LBP P 2 = 1 1 I = (2) Fgure 2. Computaton of LBP; (a) Orgnal mage regon, (b) Thresholdng step of equaton (1), (c) Bnary mask, (d) Resultng mage after usng the bnary mask. Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

4 The central pxel s replaced by the obtaned LBP value (169 n the example of fgure 2). A new LBP mage s constructed by processng each pxel and ts 3x3 neghbours n the orgnal mage. LBP can be easly extended to nclude a larger neghbourhood area, leadng to a dfferent result. 3.2 Local Ternary Pattern (LTP) LBP has been successfully used by the research communty n texture classfcaton. However LBP s senstve to nose n near unform regons. In order to solve ths problem a new texture descrptor was ntroduced recently n [18]. Ths Descrptor s called local ternary pattern (LTP). Lke the LBP, t gves an nvarant texture measure computed from the analyss of a local neghbourhood over a central pxel. LTP extends LBP to 3-valued codes, n whch local pxels havng ther gray levels wthn an nterval between a user defned threshold t and +t when compared wth the central pxel are assgned a value 0. The pxels above +t when compared to the central pxel are assgned a value 1 and the ones below t when compared to the central pxel are assgned a value -1. Equaton (3) shows how to compute the LTP: 1 f P P0 + t ' P = 0 f P P < t 0 (3) 1 f P P0 t t s a user defned threshold. Ths makes the resultng descrptor less senstve to nose but no longer strctly nvarant to graylevel transformatons. Fgure 3 shows the result of applyng LTP to an mage usng a threshold t = 5. Fgure 3. Computaton of LTP; (a) Orgnal mage regon, (b) Thresholdng step of equaton (3). In order to get rd of the negatve part of the LTP, the LTP above s splt n two LBP channels. The upper pattern (LTPU) s obtaned by replacng the negatve values wth 0. The lower pattern (LTPL) s obtaned by frst replacng the 1 wth 0 and then changng the negatve values to 1. Fgure 4 shows the resultng upper and lower patterns (LTPU and LTPL). Fgure 4. Result of splttng LTP n two LBP channels (LTPU and LTPL). Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

5 The resultng pxel s computed as for LBP by multplyng the resultng kernels wth the local weghts. Fgure 5 show how to compute the LTPU and LTPL values. Ths processng gves two texture mages enhancng dfferent characterstcs of the mage texture. Fgure 5. Computaton of LTPU, LTPL for the data n fgures 3 and SUBSPACE LEARNING AND RECOGNITION Subspace learnng dmensonalty reducton technques are a set of mathematcal technques used for representng avalable data n a low dmensonal space. The obtaned representaton s a compressed verson of the orgnal data wth the most mportant characterstcs preserved for further processng. Dmensonalty reducton seek to represent a set of data as a p-dmensonal manfold embedded n an m-dmensonal space (wth p<m) [20]. Dmensonalty reducton technques can be classfed n three types [20]: Lnear/nonlnear: based on the type of transformaton appled to the data (mappng). Local/global: based on the propertes the transformaton does preserve (n most nonlnear methods, there s a compromse between the preservaton of local topologcal relatonshps and the global structure of the data). Eucldean/geodesc: based on the dstance functon used to estmate whether two data ponts are close to each other before and after the mappng. In face recognton, dmensonalty reducton technques are used for subspace learnng and recognton of data extracted from face mages. In nfrared face recognton most of the technques used are lnear technques. In ths work we present nonlnear technques we developed for multspectral face recognton. Global and local dmensonalty reducton technques are used and ther performances compared to classcal lnear dmensonalty reducton technques. For ths purpose, the followng technques were developed: global non lnear technques (Kernel-PCA, Kernel-LDA) and local non lnear technques (Local Lnear Embeddng, Localty Preservng Proecton). 4.1 Lnear technques The most popular technques n nfrared face recognton are lnear based technques. The most used s by far the Egenfaces (PCA) technque [5],[9] followed by Fsherfaces (LDA) technque [5],[9]. These technques serve as a reference for performance comparson wth other technques. Prncpal Component Analyss (PCA) The PCA or Egenfaces technque permt a dmensonalty reducton by subspace representaton of faces. Average face mage s constructed from the tranng set and used to derve the egenvectors representng the face space approxmaton. Gven an m-dmensonal vector representaton for a face mage, the PCA can be used to fnd a subspace wth maxmum varance bass drectons n the orgnal space. Let F represent the lnear transformaton mappng the orgnal m- dmensonal space onto a n-dmensonal subspace wth n<<m. The new vector s defned by: y = F x. The columns of F are the egenvectors obtaned by solvng λ v =Qv, where Q s the covarance matrx. Under the assumpton that the tranng set s a good representaton of possble face mages, the selected vectors are a good approxmaton of all possble faces. By selectng the egenvectors correspondng to hgh egenvalues we construct a Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

6 low dmensonal proecton space of face mages. More detals about ths technque can be found n the work of Turk et al.[21]. Lnear Dscrmnant Analyss (LDA) The second most popular technque n nfrared face recognton s LDA, also known as Fsherfaces technque. Lke PCA, ths technque permts a dmensonalty reducton by subspace representaton of faces. Instead of prncpal components decomposton, n LDA the data are separated lnearly n the Egenspace usng Sngular Value Decomposton (SVD) [5],[9]. LDA searches for vectors that best dscrmnate between classes. Gven a number of features representng the data, LDA creates a lnear combnaton of these features n order to get the largest mean dfferences between the desred classes. The LDA defnes two measures [9]: Wthn-class scatter matrx: S = c N w = 1= 1 T ( x μ )( x μ ) (4) Between-class scatter matrx: S b = c = 1 T ( μ μ)( μ μ) (5) Where: x s the th sample of class, n class, μ s the mean of all classes. μ s the mean of class, c s the number of classes, N s the number of samples Wth LDA we try to maxmze between-class measure whle mnmzng wthn-class measure. We look for a lnear subspace W maxmzng the dscrmnaton crtera: 4.2 Global kernel based non lnear technques T W SbW J ( W ) = (6) T W S W Kernel based subspace learnng technques [9] have been used n order to overcome the lmtatons of lnear mappng. Kernel-PCA and Kernel-LDA are tested n ths work. The kernel trck s used to map orgnal data n a non lnear space pror to dmensonalty reducton usng PCA and LDA. In our experments we use a Gaussan kernel functon: 4.3 Local non lnear technques w 2 x y k( x, y) = exp( ) (7) 2 2σ In ths work two local non lnear technques are used for multspectral face recognton: Local Lnear Embeddng (LLE) and Localty Preservng Proecton (LPP). Local Lnear Embeddng (LLE) LLE s a non-lnear dmensonalty reducton technque. The assumpton s that that each data pont and ts neghbors le on or close to a locally lnear patch of the manfold [9],[20]. n n Gven X R a DxN matrx of the data and Y R a dxn matrx of the data n the new subspace wth N the number of data and D and d the dmensons n the orgnal space and the new subspace. In order to compute LLE, the followng steps are needed: 1. Create the adacency graph contanng the nformaton from K nearest neghbors of each data pont. 2. Compute the reconstructon weghts W. Choose the weghts to mnmze the followng cost functon: Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

7 N K = 1 = 1 3. Compute the new coordnates Y usng the weghts W : 2 J ( W ) = x W x (8) a. compute M = ( I W )( I W ) ; T b. keep the d egenvectors u assocated wth the bottom d non zero egenvalues: Localty Preservng Proecton (LPP) u1 Y = [ y y 1 K N ] = M (9) ud LPP fnds an embeddng that preserves local nformaton. LPP shares some propertes wth LLE, such as localty preservng character [9],[20]. It ams to represent the data n a lower dmensonal subspace whle preservng the local structure of the orgnal mage space. LPP s obtaned by fndng the optmal lnear approxmatons to the egenfunctons of the Laplace Beltram operator or an approxmaton to Laplacan egenmaps [9],[20]. The frst step s smlar to LLE n fndng adacent K nearest neghbors. The second step permts the computaton of a weght matrx W. The matrx values can be computed usng a kernel functon of the followng form: d N W 2 x x = e t (10) Where t s a real. The last step permts the computaton of the egenvectors usng the Laplacan by solvng the followng system: Where D s a dagonal matrx wth D = W t t XLX A = λxdx A (11) and L = D - W s the Laplacan matrx. The new representaton Y n the new subspace: obtaned from solvng equaton (11). t x y = A x where A = a ; a ; K; ad ) and a are the egenvectors ( EXPERIMENTAL RESULTS Experments were conducted on normalzed mages from the multspectral databases presented above. A face regon was extracted based on the detected eyes and mouth postons. Ths regon was then algned and normalzed usng an affne transform n a 128x128 pxels mage. The obtaned mages were processed usng LBP and LTP. The face mages contan varatons lke: facal expressons, pose, eyeglasses, facal har, etc. For LTP processng we tested dfferent thresholds, and the best results were obtaned for a threshold value equal to 5 (Only the best results are presented n the tables below). Fgures 6 and 7 show two examples of dfferent texture processng technques on the extracted nfrared face mages. Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

8 (a) (b) (c) (d) (e) Fgure 6. Processed mages n the MWIR spectrum : (a) Extracted MWIR face mage, (b) LBP texture mage, (c) LTPL texture mage, (d) LTPU texture mage and (e) Dfferental LTP texture mage. (a) (b) (c) (d) (e) Fgure 7. Processed mages n the LWIR spectrum : (a) Extracted LWIR face mage, (b) LBP texture mage, (c) LTPL texture mage, (d) LTPU texture mage and (e) Dfferental LTP texture mage. Subspace learnng was conducted for each algorthm and for all the spectrums. For each recognton algorthm, 10 tests wth 100 randomly chosen mages for recognton were conducted (overall 1000 tests were done). The obtaned results are gven n tables 1 and 2, they show the obtaned success rate for each algorthm n dfferent spectrums and wth dfferent texture processng. The result wth the orgnal mage wthout texture processng s also presented. We can see that the results are very nterestng. Texture descrptors also ncrease the recognton performance, partcularly n the nfrared spectrums (LBP and LPTH are the best performng texture descrptors technques). Recognton n the vsble spectrum for Equnox s also ncreased by texture processng. Non lnear technques are also the best performng n Equnox database. For Laval Unversty database LDA gves overall the best results. The performance of LDA n Unversty Laval database s explaned by the avalablty of more face mages for each ndvdual n the ntrapersonal learnng process. Overall the best results were obtaned for Equnox SWIR mages when usng non lnear dmensonalty reducton technques. Table 1. Results for Equnox Database. Vsble SWIR MWIR LWIR Org LBP LTPH LTPL Org LBP LTPH LTPL Org LBP LTPH LTPL Org LBP LTPH LTPL PCA 16% 87% 87% 83% 29% 71% 88% 94% 32% 72% 61% 77% 25% 57% 65% 67% KPCA 0% 4% 4% 2% 6% 17% 0% 6% 3% 3% 11% 0% 7% 0% 2% 3% LDA 2% 7% 9% 10% 76% 16% 17% 18% 0% 12% 2% 18% 5% 8% 12% 5% KDA 0% 2% 3% 3% 5% 5% 5% 6% 0% 3% 2% 3% 0% 3% 2% 2% LPP 67% 69% 89% 82% 66% 95% 95% 94% 18% 79% 74% 76% 18% 62% 73% 67% LLE 75% 86% 79% 78% 68% 96% 93% 94% 40% 81% 74% 73% 46% 40% 48% 47% Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

9 Table 2. Results for Laval Unversty Database. Vsble NIR MWIR LWIR Org LBP LTPH LTPL Org LBP LTPH LTPL Org LBP LTPH LTPL Org LBP LTPH LTPL PCA 39% 76% 87% 76% 10% 41% 66% 70% 35% 80% 79% 82% 42% 71% 75% 69% KPCA 3% 0% 1% 3% 5% 3% 4% 4% 5% 7% 1% 3% 5% 2% 2% 8% LDA 86% 80% 89% 92% 60% 68% 80% 75% 65% 86% 82% 84% 54% 79% 81% 82% KDA 2% 3% 1% 0% 4% 4% 0% 2% 2% 3% 2% 2% 2% 0% 0% 1% LPP 66% 69% 60% 68% 31% 42% 62% 50% 52% 71% 81% 65% 44% 62% 52% 65% LLE 94% 67% 66% 72% 37% 39% 42% 43% 55% 50% 57% 55% 45% 50% 46% 52% 6. CONCLUSION In ths work we ntroduced the use of LBP lke texture descrptors for effcent multspectral face recognton. The recognton algorthms n texture space outperformed the success rate obtaned for non transformed mages partcularly n the nfrared spectrum. The obtaned results are promsng and show that texture descrptors are nterestng solutons for a better multspectral face recognton system. The proposed texture space s less senstve to nose, llumnaton change and facal expressons. Work s beng conducted to adapt the above texture descrptors to a multspectral fuson scheme n order to buld a robust face recognton system. REFERENCES [1] Ross, A.A., Nandakumar K., and Jan A.K., [Handbook of Multbometrcs], Sprnger, (2006). [2] Jan, A.K., Flynn P., and Ross, A.A., [Handbook of Bometrcs], Sprnger, (2007). [3] Bronsten, A., Bronsten, M. and Kmmel. R., Three-dmensonal face recognton, Internatonal Journal of Computer Vson, 64, 5-30 (2005). [4] Kong, S., Heo., J., Abd, B., Pak, J. and Abd, M., Recent advances n vsual and nfrared face recognton: a revew, Computer Vson & Image Understandng, 97, (2005). [5] Akhlouf, M.A., Bendada, A. and Batsale, J.C., State of the art n Infrared face recognton, QIRT Journal, 5 (1), 3-26 (2008). [6] Buddharau, P., Pavlds. I.T.and Tsamyrtzs, P., Pose-Invarant Physologcal Face Recognton n the Thermal Infrared Spectrum, IEEE Conference on Computer Vson and Pattern Recognton Workshop, New York, (2006). [7] Akhlouf, M.A. and Bendada, A., "Thermal Faceprnt: A new thermal face sgnature extracton for nfrared face recognton", Proceedngs of the 5th Canadan Conference on Computer and Robot Vson (CRV 2008), Wndsor, ON, Canada, (2008). [8] Akhlouf, M.A. and Bendada, A., "Infrared Face Recognton Usng Dstance Transforms", Proceedngs of the 5th Internatonal Conference on Image and Vson Computng (ICIVC 2008), 30, (2008). [9] Akhlouf, M.A., Bendada, A. and Batsale, J.C., "Multspectral face recognton usng non lnear dmensonalty reducton", Proceedngs of SPIE Vsual Informaton Processng XVIII conference, 7341, (2009). [10] Akhlouf, M.A. and Bendada, A., "Probablstc Bayesan framework for nfrared face recognton", In proc. Internatonal Conference on Machne Vson, Image Processng, and Pattern Analyss 2009, (2009). [11] Akhlouf, M.A., Maldague, X. and Ben Larb, W., "A New Color-Texture Approach for Industral Products Inspecton," Journal of Multmeda (JMM), 3 (3), (2008). [12] Akhlouf, M.A., Ben Larb, W., and Maldague, X., "Framework for color-texture classfcaton n machne vson nspecton of ndustral products", Proceedngs of the IEEE Internatonal Conference on Systems, Man, and Cybernetcs, Montréal, Qc, Canada, (2007). [13] Oala, T, Petkanen, M. and Harwood, D., A comparatve study of texture measures wth classfcaton based on feature dstrbutons, Pattern Recognton 29 (1), (1996). Proc. of SPIE Vol Downloaded from SPIE Dgtal Lbrary on 31 Oct 2011 to Terms of Use:

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