A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION

ISSN: 0976-910(ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 013, VOLUME: 03, ISSUE: 04 A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION A. Suruland 1, R. Reena Rose and K. Meena 3 1 Department of Computer Scence and Engneerng, Manonmanam Sundaranar Unversty, Inda E-mal: suruland@yahoo.com Department of Computer Applcatons, St. Xaver s Catholc College of Engneerng, Inda E-mal: maltoreenarose@yahoo.n 3 Department of Computer Scence and Engneerng, Sardar Raa College of Engneerng, Inda E-mal: meen.nandhu@gmal.com Abstract plays a predomnant role n recognzng face mages. However dfferent persons can have smlar texture s that may degrade the system performance. Hence n ths paper, the problem of face smlarty s addressed by proposng a soluton whch combnes textural and geometrcal s. An algorthm s proposed to combne these two s. Fve texture descrptors and few geometrcal s are consdered to valdate the proposed system. Performance evaluatons of these s are carred out ndependently and ontly for three dfferent ssues such as expresson varaton, llumnaton varaton and partal occluson wth obects. It s observed that the combnaton of textural and geometrcal s enhance the accuracy of face recognton. Expermental results on Japanese Female Facal Expresson (JAFFE) and ESSEX databases ndcate that the texture descrptor Local Bnary Pattern acheves better recognton accuracy for all the ssues consdered. Keywords: Face Recognton, Features, Geometrc Features, Nearest Neghborhood Classfcaton, Ch-Square Dstance Metrc 1. INTRODUCTION Face recognton s an mportant area n machne vson, whch offers potental applcatons such as survellance, bometrc authentcaton, computer-human nteracton etc. It has receved tremendous attenton n the feld of research because there s a great varablty of face mages n facal expresson, ntensty, occluson, pose and agng [8]. Based on the property of the s extracted, face recognton algorthms are classfed nto holstc and local [3]. Holstc es such as prncpal Component Analyss (PCA), Lnear Dscrmnant Analyss (LDA), varants of LDA, margnal fsher analyss (MFA), egen regularzaton and extracton (ERE) were extensvely studed due to ther good performance and low computatonal complexty. Besdes the advantages, holstc nformaton of face mages s not effectve under llumnaton varaton, facal expresson and partal occluson [6]. Feature technques are robust to varatons n head orentaton, scale and locaton of face n the mage. But they are computatonally more expensve than holstc es [19], [1]. In both methods, t s dffcult for a sngle to unquely descrbe human face. Hence two or more s can be combned to effectvely descrbe face mages [5], [13], [3]. Therefore an dea of combnng two s es namely textural and geometry s proposed n ths paper. 1.1 MOTIVATION AND JUSTIFICATION FOR THE PROPOSED APPROACH Textural extracton methods have an mportant role n recognzng obects and scenes. They can be used to determne unformty, lghtness, densty, fneness, coarseness, roughness, regularty, etc., of texture patterns as a whole [], [10]. An ample number of methods have been proposed to extract facal texture s. Oala et al, [11] developed Local Bnary Pattern (LBP) n whch a gray scale nvarant texture pattern for a local neghborhood of 3 3 s defned. A dervatve of the LBP [1] was later ntroduced by them to descrbe rotatonal and gray scale nvarant pattern on a crcular neghborhood that could represent salent mcro-patterns of face mages. It s a very powerful method to analyze textures [19], [0]. Suruland and Ramar [18] proposed a unvarate texture model called Local Patterns (LTP) for mage classfcaton and proved that t s robust n terms of gray scale varaton and rotatonal varaton. Masly [7] suggested a method called Ellptcal Local Bnary Template (ELBT) and showed that t works well for face recognton system. Local Lne Bnary Pattern (LLBP) [1] whch was ntroduced by Amnart and Sanun s more dscrmnatve and nsenstve to llumnaton varaton and facal expresson. Shengca et al, [16] ntroduced Mult-scale Block Local Bnary Pattern (MBLBP) for face recognton and proved that ther method outperforms other LBP face recognton systems. All the textural methods dscussed above have been appled for face recognton and have produced better results. But the texture descrptors used n ths work are not yet been proved to perform better under all the ssues n face recognton. Motvated by ths, an attempt s made n ths paper to nvestgate the performance of LBP, ELBT, LLBP, LTP and MBLBP under dfferent challenges lke expresson varaton, llumnaton varaton, and varaton wth spectacles. Also, most of the texture descrptors explaned here have not yet been tested for face recognton by combnng wth geometrc s. So n ths paper, an effort s made to dentfy a texture descrptor among the aforesad descrptors that perform better n combned. In the early stages, geometrcal s were used for recognzng face mages. To determne the, certan ponts n the face are detected to form segments, permeters and area [17, ]. Yousra Ben Jemma and Sana Khanfr [1] compared the performance of geometrc dstances wth Gabor coeffcents for face recognton and have proved that ther combned yelds better results. Zhengyou et al, [] have 605

A SURULIANDI et al.: A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION evaluated the performance of facal expresson recognton system usng mult layer perceptron by combnng geometry method wth Gabor wavelets. In ther work they have reported that the combned can consderably mprove the face recognton. The face mages are acqured almost n frontal vew n real lfe stuatons such as takng photographs n drvng lcense, passport, dentty cards etc. In these cases there wll be slght expresson varaton, very less rotaton varaton, small llumnaton varaton and partal occluson wth spectacle. In such stuatons, combnng texture wth geometrc measures wll be more approprate. Justfed by ths, performances of fve texture descrptors and seven geometrcal measures are analyzed ndependently and ontly. In our early work [13] the texture descrptors LBP, ELBT and GLCM were combned wth geometrcal and t was proved that t enhances the performance of face recognton under expresson varaton. In ths paper, performance of the that combnes a few more textural methods wth geometrcal s has been studed for three dfferent ssues. 1. OUTLINE OF THE PROPOSED APPROACH Overall process of the proposed system s depcted n Fg.1. Intally, all the mages are preprocessed to algn nto same canoncal pose. In texture es certan regon of nterest s cropped from the mages n order to avod processng unnecessary detal present n the face. Durng tranng, texture and geometrcal s are extracted from every mage and are stored separately n the database. Whle testng a probe mage, texture and geometrcal s are extracted for that mage, and are matched aganst all the mages n the database usng nearest neghborhood classfer. The dssmlarty measure used to match texture s weghted ch-square [7] and geometrcal s ch-square. The performance of the proposed s studed usng texture and geometrcal s separately and as well as ontly. 1.3 ORGANIZATION OF THE PAPER Rest of the paper s organzed as follows. Secton gves a bref revew of the texture and geometrcal extracton Gallery set mages Probe mage Preprocessng Preprocessng extracton Geometrcal extracton extracton Geometrcal extracton methods used. Ths secton also explans the algorthm proposed for the combned. Secton 3 focuses on expermental setup, the results, and dscussons of the textural, geometrcal and the proposed combned for three dfferent ssues such as expresson varaton, llumnaton varaton and partal occluson wth spectacle. The conclusons are presented n secton 4.. FEATURE EXTRACTION.1 TEXTURE DESCRIPTORS s a term that characterzes the contextual property of an mage. A texture descrptor can characterze an mage as a whole as n GLCM [15]. Alternatvely, t can also characterze an mage locally at the mcro level and by global texture descrpton at the macro level. In local descrpton, the relatonshp between a pxel and ts neghborhood pxels wll be expressed n terms of local texture patterns. The occurrence frequency of such patterns wll be collected n a hstogram whch characterzes the global of the mage. The texture descrptors LBP, ELBT, LLBP, LTP and MBLBP follow the second..1.1 Local Bnary Pattern (LBP): Oala et al, [11] proposed LBP that can be used to label every pxel n the mage by thresholdng the eght neghbors of the pxel wth the center pxel value. If a neghbor pxel value s less than the threshold then a value of 0 s assgned otherwse t s 1. The result of the operaton s a bnary number as llustrated n Fg.. Bnary number can be formed startng from any poston n the neghborhood. The bnary number s then converted to decmal value and s assgned as label to the pxel. In the dervatve of orgnal LBP operator [1], the vcnty pxels can be n crcularly symmetrc neghbor sets of any radus r. The number of vcnty pxels p on the crcle wth any angle may be chosen arbtrarly. The poston of vcnty pxels (g x, g y ) can be computed usng Eq.(1) Eq.(3). In order to determne the coordnates of vcnty pxels, blnear nterpolaton can be used. Feature DB Nearest Neghborhood Classfer matchng usng weghted chsquare Combned Geometrcal matchng usng ch-square recognton Combned recognton Geometrc recognton Fg.1. Face recognton process 606

ISSN: 0976-910(ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 013, VOLUME: 03, ISSUE: 04 3 54 100 39 39 1 6 1 50 Pattern: 10010110 Decmal: 1 7 +0 6 +0 5 +1 4 +0 3 +1 +1 1 +0 0 = 150 Fg.. Computaton of label usng LBP operator g x R cos, g R sn (1) y r R, () r sn r cos 4 360 1 (3) p An LBP can be classfed as unform or non unform pattern. It s sad to be unform f only t contans at most two transtons from 0 to 1 or vce versa n the bnary pattern. Usage of unform patterns reduces the total number of bns requred. Image analyss requres p(p-1)+ bns for unform pattern and one extra bn for all non unform patterns thus requres a total of p(p-1)+3 bns..1. Ellptcal Local Bnary Template (ELBT): Masly [7] suggested ths method whch s very smlar to that of LBP. The only dfference s that vcnty pxels le on an ellpse relatng to the central pxel rather than on a crcle. To calculate coordnates of vcnty pxels, vertcal radus (vr) as well as horzontal radus (hr) s requred. Here R s computed as n Eq.(4). hr * vr R, (4) hr sn vr cos.1.3 Local Lne Bnary Pattern (LLBP): LLBP researched by Amnart Petpon and Sanun Srsuk [1] dffers from LBP n two aspects: 1) Neghborhood pxels consdered are those that le n a straght lne ether horzontally or vertcally. ) Startng from the adacent pxel of the center pxel c, bnary weghts are dstrbuted as n Fg.3. Three measures such as LLBP operator for horzontal lne (LLBPh), vertcal lne (LLBPv) and ts magntude (LLBPm) are calculated for every pxel n the mage by usng Eq.(5) Eq.(7). The Fg.3 llustrates the operaton of LLBP n horzontal drecton. Smlar operaton can be done n vertcal drecton also. n = 1 n = c n = N 1 111 3 100 1 11 33 113 4 1 148 0 0 0 0 0 0 11 1 0 0 0 0 1 Threshold=39 5 4 3 1 0 11 0 1 3 4 5 LLBPh = (0+0+0+0+0+0) + (1+0+0+0+0+3) = 33 Fg.3. LLBP operator n horzontal drecton wth lne length of 13 pxels 0 1 1 1 0 0 0 1 Intensty value of pxels along horzontal drecton Thresholded value Bnary Weght where, and h v LLBPm LLBP LLBP (5) LLBP LLBP v c 1 c n 1 N, c n 1 s h n h c N n c 1 n c 1 s h n h c c 1 c n 1 N, c n 1 s v n v c n c 1 s v v h N n c 1 n c (6) (7) s ( x) 1 f x 0 (8) 0 f x 0 In the above equatons, c s the poston of the center pxel, N s the length of the lne and s s as represented n Eq.(8)..1.4 Local Pattern (LTP): Suruland and Ramar[18] have proposed LTP. In ths, every pxel s assgned wth a label that s computed wth the pattern unts P obtaned for ts eght neghbors as descrbed below. f g g c g g c g g g c g f g g g 0 P( g, g c ) 1 f 1,,...,8. (9) 9 c In the above equaton, g c s the gray value of center pxel and g s the gray value of 3 3 neghbors and g s a small postve nteger value that has an mportant role n formng the unform patterns. A pattern strng s then formed by collectng the P values of the eght neghbors startng from any poston as descrbed below. 8 P g, g f 3 LTP U 1 c (10) 73 otherwse U s the unformty measure and s defned as, where, U s Pg8, gc, Pg1, gc spg, g, Pg, g 8 c 1 c (11) 1 f x y 0 s ( x, y) (1) 0 otherwse Number of unque patterns whch can be obtaned by LTP scheme s 46 n total and they can have values n the range 0 to 73 leavng few holes n between. Therefore patterns are relabeled to form contnuous numbers from 1 to 46 by usng a lookup table..1.5 Mult-scale Block Local Bnary Pattern (MBLBP): MBLBP ntroduced by Shengca Lao et al [16] can be used to obtan texture pattern for every pxel by consderng a local regon of sze 3 3, 9 9, 15 15 etc. wth center pxel. Computaton of MBLBP for 3 3 local regon s equvalent to the orgnal LBP. Local regon of other szes can be decomposed nto equally szed regons. And then the average sum of pxel ntensty for every sub regons s calculated, whch s then thresholded wth the center regon average value. Thereafter MBLBP values are computed n a smlar manner as n LBP. An example for the calculaton of MBLBP s shown n Fg.4. 607

A SURULIANDI et al.: A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION 11 7 63 3 7 105 111 75 16 55 0 11 3 78 9 1 6 84 73 9 04 11 11 33 01 61 115 11 99 63 1 76 36 4 54 3 45 Fg.4. MB-LBP for 9 9 sub mage.1.6 Global Descrpton: 4 17 1 54 119 76 3 55 67 110 3 13 87 8 6 Process of formng global texture descrpton s as follows For every pxel n the mage, select ts s neghborhood pxels of predefned sze n n. Compute local texture for the neghborhood usng any one of the local texture descrptor. Collect the occurrence frequency of every local texture pattern n a one dmensonal hstogram that characterzes the global texture descrpton of the mage.. GEOMETRICAL MEASURES In ths, mportant face components and/or ponts are selected n the face mages. A vector s formed wth the dstances between those ponts or the relatve szes or poston of the components [17], []. In ths work, 13 facal ponts are selected manually and the dstances between those ponts n terms of pxels are computed to dentfy maor face components such as nose wdth (p5-p11), mouth wdth (p6- p1), dstance between rs centers (p3-p9), dstance between the nose and mouth (p13-p), dstance between the center pont of the lne connectng the rs centers and nose (p-p8), face heght (p7-p1) and face wdth (p4-p10). These geometrcal measures have a maor role n dscrmnatng dfferent face mages because they characterze all the facal components. Performance of face recognton systems may deterorate f the manual selecton s replaced by automatc one [3]. Fg.5. depcts the selecton of fducal ponts. p8 p9 p10 p11 p1 p13 14 113 3 6 5 5 31 43 11 37 87 7 113 1 6 16 46 57 0 110 31 17 00 15 18 67 89 63 75 44 96 79 105 Average sum of every sub regon Fg.5. Fducal ponts selected for geometrcal.3 PROPOSED TECHNIQUE FOR COMBINING FEATURES Followng procedure s used for combnng texture and geometrc s. p1 p p3 p4 p5 p6 p7 0 0 0 0 0 1 0 1 MBLBP value by thresholdng wth center regon average 1) Geometrc s (GF) n the form of vector and texture s (TF) n the form of hstogram are computed for gallery set mages and are stored n a database. ) For every probe mage do the followng:. Determne GF and TF for the mage... Fnd out the dssmlarty between the probe mage and every mage n the gallery set for GF usng Ch-square statstc as defned below. O E O, E (13) O E where, O s the th value of the gallery set mage and E s the th value of the probe one. Snce certan regon n the face have more mportance, compute the dssmlarty among the mages for TF usng weghted Ch-square defned n Eq.(14). O E,, wo, E w, (14) O E,, In the above formula O, and E, are the th of th regon n the gallery and probe mage respectvely. w s the weght of th regon as show n Fg.6(c). v. Normalze dssmlarty measure for GF ndvdually for the mages n the gallery set by applyng the followng equaton, O, E/ max O, E mn O E N GF, where, N GF s the normalzed dssmlarty measure for every mage n the gallery set, (O, E) s the dssmlarty measure, max (O, E) and mn (O, E) are the maxmum and mnmum dssmlarty measures among the gallery set mages respectvely. v. Normalze dssmlarty measures for TF ndvdually for the mages n the tranng set by applyng the followng equaton, v. v. N O, E max O, E mn O E TF w w w, where, N TF s the normalzed dssmlarty measure for every mage n the gallery set, O E s the dssmlarty measure, max w, O E and mn O E w, are the maxmum w, and mnmum dstance measures among the gallery set mages respectvely. Add the two normalzed measures N GF and N TF for every mage n the gallery set. The gallery mage whch yelds least dssmlarty measure wth the probe mage s consdered as the recognzed one. 608

ISSN: 0976-910(ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 013, VOLUME: 03, ISSUE: 04 3. EXPERIMENTAL RESULTS AND DISCUSSIONS Preprocessng s requred snce most of the mages n the databases are orented n some drecton. Therefore all the mages are rotated n such a way that a lne connectng rs centers les n a horzontal lne. For textural methods, a subspace s chosen by applyng face anthropometrc measure (dstance between rs centers) as n Fg.6(a) to avod the computatonal burden of usng entre face. Eq.(15) Eq.(18) are used to crop the subspace that contans necessary detals n the face. x 1 = x - (p/) (15) y 1 = y - p (16) x = (x + 3 * p) (p/) (17) y = (y + 3 * p) (p/4) (18) where, p s half the dstance between two rs centers Faces can be effectvely represented f the subspace s dvded nto sub regons and matchng s done between respectve regons n gallery as well as probe mage. Therefore only for the textural methods, 49 equally szed sub regons [14, 19] are formed as shown n Fg.6(b). Certan regons cover more mportant facal s and are assgned dfferent weghts as n Fg.6(c). Followng expermental setup and parameter settngs are used n the local texture descrptors. Wth LBP, two experments are conducted, one wth p as 16 (LBP 16 ) and other wth p as 8 (LBP 8 ). For LBP 8, total number of bns requred s 59 [8*(8-1) + 3] and for LBP 16 t s 43 [16*(16-1) + 3]. For LTP 8 and LTP 16 the parameter g s set to 5. ELBT operator s appled on 16 neghbor pxels at horzontal radus 3 and vertcal radus. In LLBP, horzontal and vertcal lnes wth length 13 are consdered. In MBLBP, the sze of the local regon consdered s 9 9. Values assgned to the parameters are the one that have gven best results. (a) (c) Fg.6. (a). Subspace selecton wth face anthropometrc measure (b). 7 7 regons of face mage (c). Weghts assgned to every regon [7] (b) 3.1 RESULTS ON EXPRESSION VARIATION Robustness n face recognton under facal expresson varatons s the most challengng ssue. Facal expressons result n temporally deformed facal s that lead to false recognton. Therefore effectveness of the proposed s tested under expresson varaton by usng JAFFE database [9]. The database contans 13 frontal face mages of 10 Japanese female models wth seven dfferent expressons. Sample mages are dsplayed n Fg.7. Experment s conducted by settng all the neutral expresson mages n the gallery set and the rest of the mages n the probe set. The results are tabulated n Table.1. Neutral Sadness Surprse Anger Dsgust Fear Happness Fg.7. Sample mages from JAFFE database Table.1. Recognton Accuracy under Expresson Varaton Local Descrptors recognton Recognton n percentage Geometrc recognton Combned (+ Geometrc) 9.09 LBP 16 90.39 LBP 8 68.9 75.14 ELBT 84.18 86.44 LLBP 80.79 51.41 8.48 LTP 16 78.53 86.44 LTP 8 64.40 7.31 MBLBP 69.49 79.66 The results n the table show that among the tested texture methods, LBP 16 produces better result of 90.39% recognton accuracy. It s noted that ths accuracy s % greater than that of LBP 8. Geometrcal s fal to produce better results because facal expressons are produced by changes made n the shape of facal components, especally the mouth and eyes. Ths causes the geometrcal measures to produce lower recognton accuracy of 51.41%. It s also observed that the combnaton of texture and geometrcal s produce better results than the ndvdual one for facal expressons. 3. RESULTS ON ILLUMINATION VARIATION Recognton under dfferent lghtng condton s a challengng problem n computer vson. Ths varaton n llumnaton affects the classfcaton greatly. Therefore n ths paper, performance of the proposed system s evaluated by conductng an experment on llumnaton varaton mages. Frontal mages wth controlled llumnaton varaton of 7 persons are taken from ESSEX database [4]. One exemplar per person s kept n the gallery set and 9 mages per ndvdual are kept n the probe set. Some of the mages used for the 609

A SURULIANDI et al.: A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION experment are shown n Fg.8. Expermental results are gven n Table.. texture descrptors perform better n combned. For the experments the regons of eyes wth spectacle frame s gven more weght. And for dfferent persons those regons are same. Therefore, when two persons wear smlar spectacle, that regon nformaton can be smlar for both. Ths can be one of the reasons for the texture descrptor not to recognze some mages. Fg.8. Sample mages used for Illumnaton varaton from ESSEX database Table.. Recognton Accuracy under Illumnaton Varaton Local Descrptors recognton Recognton n percentage Geometrc recognton Combned (+ Geometrc) 100 LBP 16 99.58 LBP 8 96.9 96.70 ELBT 99.58 100 LLBP 95.88 66.66 97.94 LTP 16 97.53 97.94 LTP 8 94.3 95.06 MBLBP 90.53 91.35 Expermental results ndcate that, for llumnaton varant mages, LBP 16 and ELBT produce 99.58% recognton accuracy for the probe mages. Ths shows the effectveness of LBP 16 and ELBT n recognzng llumnaton varant mages. It s also observed that even wth seven geometrcal measures the system gans 67% recognton accuracy. Ths s an evdent that shows the effcency of the seven geometrcal measures chosen. In addton, t s notced that the combned yelds better results than the ndvdual ones. 3.3 RESULTS ON PARTIAL OCCLUSION WITH OBJECTS Occlusons appear as local dstorton away from a common face representng human populaton [6]. In order to study the capablty of the es on recognzng faces occluded wth obects, frontal face mages of 13 persons wth spectacles are collected from ESSEX database [4]. Three mages per ndvduals are chosen n random as gallery set and 10 mages per person are kept n the probe set. Sample mage used for the experments are gven n Fg.9. Table.3 gves the expermental results. Expermental results demonstrate that the texture descrptor LBP 16 produces hgher recognton accuracy of 96.15% for faces partally occluded wth spectacles. Ths shows that LBP 16 s more suted than the other tested texture descrptors n recognzng faces partally occluded wth spectacles. Wth the gven seven measures, the geometrcal method s able to acheve recognton accuracy of 7%. Ths shows the effectveness of the geometrcal measures chosen. It s also notced from the test results that except LBP 8, LLBP and MBLBP, all the other Fg.9. Sample mages used for Varaton wth Spectacles from ESSEX database Table.3. Recognton Accuracy under Varaton wth Spectacles Local Descrptors recognton Recognton n percentage Geometrc recognton Combned (+ Geometrc) 97.69 LBP 16 96.15 LBP 8 94.61 94.61 ELBT 95.38 96.9 LLBP 95.38 7.30 95.38 LTP 16 93.07 94.61 LTP 8 93.07 93.84 MBLBP 9.30 9.30 4. CONCLUSION s capture the mcro prmtve patterns present n the face and geometrcal s descrbe the shape detals of the facal components. But when they are appled alone, face recognton may not produce good results. Therefore ths paper proposes to combne those two s to enhance the accuracy of face recognton. Performances of fve texture extracton methods LBP, LTP, ELBT, LLBP and MBLBP and geometrcal methods are nvestgated ndependently and combned together. Face recognton ssues such as llumnaton varaton, expresson varaton and varaton wth spectacle are addressed n ths work. Expermental results demonstrate that LBP 16 provdes more accuracy of recognton than the other textural methods for all the ssues dscussed. Ths s due to ts ablty n determnng many number of mportant local texture prmtves. Moreover for all the ssues concerned, the combned of LBP 16 wth geometrcal s produces better recognton accuracy. LBP 16 produces better results at the cost of hgh computatonal complexty due to more number of bns. Because of smple computatons and less number of bns used, LTP s the one that produces results faster than the other methods tested. 610

ISSN: 0976-910(ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 013, VOLUME: 03, ISSUE: 04 LTP also produces better results wth 97.53% accuracy for llumnaton varaton and 93.07% accuracy for varaton wth spectacle. Face recognton under varyng expresson requres more attenton owng to low results produced by all the methods tested. In future, the methods expermented here can be used to evaluate the performance of face recognton systems under ssues that are not consdered n ths work such as agng, pose varaton, and faces partally occluded wth obects other than spectacle. A new texture method that mproves recognton accuracy wth less number of bns can be evolved from the methods tested. Performance of more number of geometrcal measures can be analyzed for the combned n future. REFERENCES [1] Amnart Petpon and Sanun Srsuk, Face Recognton wth Local Lne Bnary Pattern, Ffth IEEE Internatonal Conference on Image and Graphcs, pp. 533-539, 009. [] Andrze Materka and Mchal Strzeleck, Analyss Methods A Revew, Techncal Report, Insttute of Electroncs, Techncal Unversty of Lodz, Brussels, pp. 1-33,1998. [3] Cong Geng and Xudong Jang, Fully Automatc Face Recognton Framework Based on Local and Global Features, Machne Vson and Applcatons, Vol. 4, No. 3, pp. 537-549, 013. [4] Face Recognton Data, Unversty of Essex, UK, The Data Archeve, Avalable at: http://cswww.essex.ac.uk/mv/allfaces/ndex.html [5] Fasal R. Al-Osam, Mohammed Bennamoun and Amal Man, Spatally Optmzed Data-level Fuson of and Shape for Face Recognton, IEEE Transactons on Image Processng, Vol. 1, No., pp. 859-87, 01. [6] Hamdreza Rashdy Kanan and Karm Faez, Recognzng faces usng Adaptvely Weghted Sub-Gabor Array from a sngle sample mage per enrolled subect, Image and Vson Computng, Vol. 8, No. 3, pp. 438-448, 010. [7] R. V. Masly, Usng Local Bnary Template Faces Recognton on Gray-Scale Images, Informatonal Technologes and Computer Engneerng, No. 4, 008. [8] Matthew Turk and Alex Pentland, Egenfaces for Recognton, Journal of Cogntve Neuroscence, Vol. 3, No. 1, pp. 71-86, 1991. [9] Mchael J. Lyons, Shgeru Akamatsu, Myuok Kamach and Jro Gyoba, Codng Facal Expressons wth Gabor Wavelets, Proceedngs of the IEEE Internatonal Conference on Automatc Face and Gesture Recognton, pp. 00-05, 1998. [10] Mhran Tuceryan and Anl K. Jan, Analyss, The Handbook of Pattern Recognton and Computer Vson, World Scentfc Publshng Co., pp. 07-48, 1998. [11] T. Oala, M. Petkanen and D. Harwood, A comparatve study of Measures wth Classfcaton on Featured Dstrbuton, Pattern Recognton, Vol. 9, No. 1, pp. 51-59, 1996. [1] T. Oala, M. Petkanen and T. Maenpaa, Multresoluton gray-scale and rotaton nvarant texture classfcaton wth local bnary patterns, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 4, No. 7, pp. 971 987, 00. [13] R. Reena Rose and A. Suruland, Combnng wth Geometry for Performance Enhancement of Facal Recognton Technques, IEEE Internatonal Conference on Emergng Trends n Electrcal and Computer Technology, pp. 80-85, 011. [14] R. Reena Rose and A. Suruland, Improvng Performance of Face Recognton Systems by Segmentng Face Regon, ACEEE Internatonal Journal on Network Securty, Vol., No. 3, pp. 3-7, 011. [15] Robert M. Haralck, K. Shanmugam and Its hak Dnsten, Textural s for Image Classfcaton, IEEE Transactons on Systems, Man and Cybernetcs, Vol. SMC- 3, No. 6, pp. 610-61, 1973. [16] Shengca Lao, Xangxn Zhu, Zhen Le, Lun Zhang and Stan Z. L., Learnng Mult-scale Block Local Bnary Patterns for Face Recognton, Proceedngs of Internatonal Conference ICB, Advances n Bometrcs, Lecture Notes n Computer Scence, Vol. 464, pp. 88 837, 007. [17] V. V. Starovotov, D. I. Samal and D. V. Brluk, Three Approaches for Face Recognton, The 6-th Internatonal Conference on Pattern Recognton and Image Analyss, pp. 707-711, 00. [18] A. Suruland and K. Ramar, Local Patterns A Unvarate Model for Classfcaton of Images, 16 th Internatonal Conference on Advanced Computng and Communcatons, pp. 3-39, 008. [19] Tmo Ahonen, Abdenour Hadd and Matt Petk anen, Face Descrpton wth Local Bnary Patterns: Applcaton to Face Recognton, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 8, No. 1, pp. 037-041, 006. [0] Top Maempaa and Matt Petk anen, Analyss wth Local Bnary Patterns, Chapter 1, WSPC/Trm sze 9.75n 6.5n for Revew Volume, Vol. 8, No. 19, pp. 1 0, 004. [1] Yousra Ben Jemaa and Sana Khanfr, Automatc Local Gabor s extracton for face recognton, Internatonal Journal of Computer Scence and Informaton Securty, Vol. 3, No. 1, 009. [] Zhengyou Zhang, Mchael Lyons, Mchael Schuster and Shngeru Akamatsu, Comparson Between Geometry- Based and Gabor-Wavelets-Based Facal Expresson Recognton usng Mult-Layer Perceptron, Thrd IEEE Internatonal Conference on Automatc Face and Gesture Recognton, pp. 454-459, 1998. [3] W. Zhao, R. Chellappa, P. J. Phllps and A. Rosenfeld, Face recognton: A lterature survey, ACM Computng Surveys, Vol. 35, No. 4, pp. 399-458, 003. 611