Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions

Transcription

1 llumnaton Normalzaton for Robust Face Recognton Aganst Varyng Lghtng Condtons Shguang Shan, Wen Gao, Bo Cao, Debn Zhao C-SVSON JDL, nsttute of Computng echnology, CAS, P.O.Box 274, Beng, Chna, 18 Computer College, Harbn nsttute of echnology, P.O.Box 321, Harbn, Chna, 151 {sgshan, wgao, bcao, Abstract Evaluatons of the state-of-the-art of both academc face recognton algorthms and commercal systems have shown that recognton performance of most current technologes degrades due to the varatons of llumnaton. hs paper nvestgates several llumnaton normalzaton methods and proposes some novel solutons. he man contrbuton of ths paper ncludes: (1) A Gamma ntensty Correcton (GC) method s proposed to normalze the overall mage ntensty at the gven llumnaton level; (2) A Regon-based strategy combnng GC and the Hstogram Equalzaton (HE) s proposed to further elmnate the sde-lghtng effect; (3) A Quotent llumnaton Relghtng (QR) method s presented to synthesze mages under a pre-defned normal lghtng condton from the provded face mages captured under non-normal lghtng condton. hese methods are evaluated and compared on the Yale llumnaton face database B and Harvard llumnaton face database. Consderable mprovements are observed. Some conclusons are gven at last. 1. ntroducton Face recognton technologes have a varety of ongong and potental applcatons n publc securty, law enforcement and commerce, such as mug-shot database matchng, dentty authentcaton for credt card or drver lcense, access control, nformaton securty, and ntellgent survellance. n addton, there are many emergng felds that can beneft from face recognton technology, such as the new generaton ntellgent humancomputer nterfaces and e-servces, ncludng e-home, tele-shoppng and tele-bankng. Related research actvtes have sgnfcantly ncreased over the past few years [1,2, 3]. As for the early researches, both geometrc feature based methods and template-matchng methods were regarded as typcal technologes, whch were compared by Brunell and Poggo n And the result of the comparson revealed that template matchng outperforms the geometrc feature based ones [2]. herefore, snce the 199s, appearance based methods have been playng a domnant role n the area, from whch a number of technologes were derved: holstc appearance feature based, and analytc local feature based. Popular methods belongng to the former paradgm nclude Egenface [4, 5], Fsherface [6], Probablstc and Bayesan matchng [7, 8, 9], subspace LDA [1], and Actve Shape/Appearance Models (ASMs/AAMs)[11,12,13] based methods. Local Feature Analyss (LFA)[14] and Elastc Bunch Graph Matchng (EBGM)[5, 15] are typcal nstances of the latter category, among whch LFA has been developed to the most successful commercal face recognton system, named Facet, by dentx Corp. FERE evaluaton has provded extensve comparsons of these algorthms [16, 17] as well as a knd of evaluaton protocol for face recognton systems. More recently, Support Vector Machne (SVM) has also been appled to face recognton successfully [18]. However, face recognton remans a dffcult, unsolved problem n general. he performance of almost all current face recognton systems, both the best academc results and the most successful commercal systems, s heavly subect to the varatons n the magng condtons. t has been dscovered by the FERE and FRV2 test that pose and llumnaton varatons are among the several bottlenecks for a practcal face recognton system [16]. So far, no revolutonary and practcal solutons are avalable for these problems. However, some solutons to pose and llumnaton problems do have emerged ncludng nvarant feature based methods [19], 3D lnear llumnaton subspace [6], lnear obect class [2], llumnaton and pose manfold [21], Symmetrc Shape-From-Shadng [1], photometrc algnment [22], Quotent mage [23], llumnaton cones [24], Lambertan Reflectance and Lnear Subspace [25], lght-felds [26] and parametrc lnear subspace [27] and ndvdual PCA combnng the syntheszed mages [28, 29]. Generally, the approaches for copng wth varaton n appearance due to llumnaton fall nto three categores: nvarant features, varaton modelng, and canoncal form [28]. he frst approach seeks to utlze features that are nvarant to the changes n appearance. Examples of such Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

2 representaton consdered by early researchers are edge maps, mage ntensty dervatves, and mages convolved wth 2D Gabor-lke flers. However, Adn s emprcal study shows that None of the representatons consdered s suffcent by tself to overcome mage varatons because of a change n the drecton of llumnaton [19]. Most recently, the Quotent mage [23] s reported to be nvarant to llumnaton and may be used to recognze faces when lghtng condtons change. he dea of varaton modelng s to learn the extent of the varaton n some sutable subspace or manfold. Recognton s then conducted by choosng the subspace or manfold closest to the novel mage. Currently, ths paradgm has been recognzed as the domnant [2, 21, 22, 24, 25, 26, 27]. n addton, snce subspaces/manfolds must be learnt by suffcent examples, some work has been done to enlarge a small learn set by vrtually remagng the nput face mage [28, 29, 31]. he canoncal form approaches attempt to normalze the varaton n appearance, ether by mage transformatons or by syntheszng a new mage from the gven mage n some normalzed form. Recognton s then performed usng ths canoncal form. Examples of ths approach nclude [1, 3]. he most commonly used Hstogram Equalzaton (HE) belongs to ths category too. hs paper nvestgates several llumnaton normalzaton methods belongng to the canoncal form framework. he man contrbuton of ths paper ncludes: (1) We present a Gamma ntensty Correcton (CC) method to normalze the overall mage ntensty to a gven ntensty level; (2) Regon-based strategy s proposed to further elmnate the sde-lghtng effect by combnng GC and Hstogram Equalzaton (HE); (3) A Quotent llumnaton Relghtng (QR) method s nvestgated to synthesze an mage under normal lghtng condton from the provded face mages captured under known nonnormal lghtng condton. hese methods are then compared on the Yale face database B and Harvard face database, n whch faces are captured under wellcontrolled llumnaton condtons. he experments show that QR can sgnfcantly mprove the performance of the face recognton systems as long as the lghtng modes of the mages are known. However, generally, the regonbased method combnng HE wth GC s practcally effectve when t s hard to estmate the lghtng modes of the nput mages. hs paper s organzed as follows: n Secton 2, the general computatonal framework for llumnaton normalzaton s dscussed followed by one nstance, GC, as well as ts combnaton wth the regon-based strategy. Secton 3 manly descrbes the proposed QR methods. Experments and conclusons are gven n the followng two sectons. 2.General Computatonal Framework For llumnaton Normalzaton Frstly, we formulate the general computatonal framework for llumnaton normalzaton method. Let fk be any gven face mage of the face f, captured under some unknown lghtng condton k. llumnaton normalzaton method attempts to obtan a face mage fo whch s the mage of the same face f captured under the pre-defned known lghtng condton,, by fndng a transform,, satsfyng: = ). (1) fo ( fk After ths transform, all the face mages to be processed are vrtually captured under the same lghtng condton. herefore, the recognton system s expected to be nsenstve to the varyng lghtng. n fact, the commonly used Hstogram Equalzaton (HE) can be categorzed nto ths framework. n the followng, we propose Gamma ntensty Correcton (GC) method. 2.1 Gamma ntensty Correcton (GC) Gamma correcton s a technque commonly used n the feld of Computer Graphcs. t concerns how to dsplay an mage accurately on a computer screen. mages that are not properly corrected can look ether bleached out, or too dark. Gamma correcton can control the overall brghtness of an mage by changng the Gamma parameter. Unlke the tradtonal Gamma correcton technque n Computer Graphcs, but motvated by ts dea, we propose the Gamma ntensty Correcton (GC) method to correct the overall brghtness of the face mages to a pre-defned canoncal face mages. t s formulated as followng: Predefne a canoncal face mage,, whch should be lghted under some normal lghtng condton. hen, gven any face mage,, captured under some unknown lghtng condton. ts canoncal mage s computed by a Gamma transform pxel by pxel over the mage poston y: * = G( ; γ ), (2) where the Gamma coeffcent γ * s computed by the followng optmzaton process, whch ams at mnmzng the dfference between the transformed mage and the predefned normal face mage : * 2 γ = arg mn [ G( ; γ ) ( ], (3) where γ y s the gray-level of the mage poston y; and G 1 γ ( ; γ ) c =, Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

3 s the Gamma transform; c s a gray stretch parameter, and γ s the Gamma coeffcent. From equaton 2 and 3, ntutvely, the GC s expected to make the overall brghtness of the nput mages best ft that of the pre-defned normal face mages. hus, ts ntutve effect s that the overall brghtness of all the processed face mages s adusted to the same level as that of the common normal face. See the experments part for ts ntutve effect. 2.2 Regon-based Strategy for HE and GC t s obvous that both HE and GC are global transforms over the whole mage area. herefore, they are doomed to fal when sde lghtng exsts. o partly solve ths problem, we propose to process the face mages based on dfferent local regons, that s, performng HE or GC n some pre-defned face regons n order to better allevate the hghlght, shadng and shadow effect caused by the unequal llumnaton. deally, t s expected to strctly partton the face accordng to the structure of the facal organs, for nstance, as llustrated n Fgure 1. Fgure 1. An example of deal regon partton However, complex regon partton needs complcated regon segmentaton approach, whch s often mpractcal. And, snce the possble sde lghtng manly cause the nonsymmetry between the left and rght part of the face, as well as the ntensty varance between the top regon and the bottom regon. n our strategy, we smply partton the face nto four regons accordng to the gven eye centers as shown n Fgure 2. Fgure 2. he four regons for llumnaton normalzaton After the coarse partton of the face regons, HE or GC can be conducted n the four regons separately. Hereafter, we abbrevate the regon-based HE to RHE, and the regon-based GC to RGC. he effects of the RHE and RGC can be seen from Fgure 4 and Fgure 7 n the expermental part. 3. Quotent llumnaton Relghtng (QR) Both HE and GC are gray-level transform approaches wthout consderng the magng model. herefore, as we can see from Fgure 4 and Fgure 7, they cannot essentally remove the sde lghtng effect. n ths secton, we present a Quotent llumnaton Relghtng (QR) method based on some of the concepts n the well-known Quotent mage method proposed by Shashua etc [23] most recently. 3.1 Background and Defntons As a class of obect, faces can be regarded as Lambertan surface,.e., the face mage can be descrbed by the product of the albedo and the cosne angle between a pont lght source and the surface normal: ( = ρ( s (4) where ρ ( 1 s the surface reflectance assocated wth pont y n the mage, s the surface normal drecton assocated wth pont y n the mage, and s s the lght source drecton (pont lght source) and whose magntude s the lght source ntensty [23]. Based on ths Lambertan model, Shashua further defnes the deal class of Obects [23] as a collecton of 3D obects that have the same shape but dffer n the surface albedo functon. hough faces do have dfferent 3D shapes, however Shashua et al show that one can tolerate sgnfcant shape changes wthout notceable degradaton n performance even there s no need to establsh any dense algnment among the mages beyond the algnment of the center of mass and scale. Based on these assumptons, recognton problem and re-renderng problem s further defned by ntroducng a bootstrap set n [23]. n ths paper, we propose the Quotent llumnaton relghtng method based on the deal class of obects. 3.2 Quotent llumnaton Defnton 1. deal class of Obects [23]. An deal class s a collecton of 3D obects that have the same shape but dffer n the surface albedo functon. he mage space of such a class s represented by: ρ ( x, s (5) where ρ ( s the albedo of obect of the class, s the surface normal of the obect (the same for all obects of the class), and s s the lght source drecton, whch can vary arbtrarly. Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

4 Defnton 2. Quotent llumnaton. Let S (pont lght source) be the pre-defned canoncal lghtng condton. he quotent llumnaton for the lghtng condton S of an deal class of obects (whose shape s n) s: R ( where y range over the whole mage. = (6) Obvously, the Quotent llumnaton s completely ndependent of the surface reflectance (albedo), and depends only on the varance of the lghtng condton from the pre-defned canoncal lghtng one (consderng all the shapes s assumed to be the same). hus, quotent llumnaton can be computed easly by calculatng the quotent between the mages of the obect of the deal class of obects as explaned by Equ.7: ρ ( ( R ( = =, (7) ρ ( ( where y range over the whole mage, s the mage of obect captured under the -th lghtng condton, and s the mage of the same obect captured under the canoncal lghtng condton. Equaton 7 provdes a practcal way to compute the Quotent llumnaton drectly from face mages wthout needng to separate the reflectance from the lghtng. However, as s well known, faces are not strctly deal class of obects snce the 3D shapes of faces are dfferent despte ther approxmate smlarty. herefore, a learn set to cover all knds of 3D face shapes s expected. Defnton 3. Quotent llumnaton Bootstrap Set. Quotent llumnaton bootstrap set s a set of pars of face mages captured under some non-canoncal lghtng condton and under the pre-defned canoncal lghtng condton,.e., {(, ) = 1,2,..., N; 1,2,..., L}. = Gven such a bootstrap set, Quotent llumnaton can be statstcally modeled, or computed smply as the mean over all the faces n the set, for nstance: N 1 ( R ( =, =1,, L N = 1 ( where y range over the whole mage. 3.3 Quotent llumnaton Relghtng (QR) After defnng the deal class of obects and Quotent llumnaton, for face obect case, llumnaton normalzaton s formulated as followng. Gven an nput face mage we assume that t s the mage of the -th face taken under -th lghtng condton. Our goal s to relght the face to obtan,.e., the mage of the face taken under the canoncal lghtng condton. hs can be done by the followng Proposton. Proposton 1. Gven an mage of arbtrary face,. Assume that t s lghted by the -th known lghtng condton, and the -th quotent llumnaton R has been computed too. hen, ts canoncal mage captured under the pre-defned -th lghtng condton can be derved by: ( ( R ( =, (8) where y range over the whole mage. Proof. Accordng to Equaton 5, 6 and 7, we have: ρ ( ( R ( = = =. ρ( ( So: ( ( R ( =, where y range over the whole mage. Proposton 1 provdes a drect and smple way for llumnaton normalzaton provded that the drecton of the lghtng source of the mage can be known. See Fgure 4 and 7 for ts ntutve effect, obvously, QR has sgnfcantly elmnate the unequal brghtness effect caused by the strong sde lghtng. 4. Experments n ths secton, we present the experments to evaluate the above-mentoned llumnaton normalzaton methods usng two publc face database specalzed on llumnaton varaton,.e., the Yale face database B and the Harvard face database. Lghtng condtons n both of the database have been systematcally controlled. 4.1 Classfcaton method Snce our goal s to compare the performances of dfferent llumnaton normalzaton methods, the dstance measurement and classfcaton method are not mportant for us. herefore, the smplest normalzed correlaton,.e., cosne of the angle between two mage vectors, s exploted as the dstance measurement,.e., the smlarty between two mages and k s defned as: Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

5 Φ (, ) = cos( <, > ) = k k And for all experments, classfcaton s performed usng the nearest neghbor classfer. n addton, as we can see from the example mages n the followng sectons, all the faces are cropped to remove the background and the har. 4.2 Results on Yale face database B Yale face database B s publcly avalable for studyng pose and llumnaton problem n face recognton. Snce ths paper manly deals wth the llumnaton problem, we only choose the 64 frontal mages captured under 64 dfferent lghtng condtons for each of the ten persons. Example mages of one person n frontal pose are shown n fgure 3. he mages are dvded nto fve subsets accordng to the angle that the lght source drecton makes wth the camera axs Subset 1(up to 12 o ), Subset 2(up to 25 o ), Subset 3(up to 5 o ), Subset 4(up to 77 o ), and Subset 5(up to 9 o ). See [24] for detals. Experments are then conducted on the database wth the above-mentoned llumnaton normalzaton methods ncludng: HE: Hstogram equalzaton globally over the mages; RHE: Regon-based Hstogram equalzaton; GC: Gamma ntensty Correcton globally. he mean face of all the mages from the subset 1 of the ten persons (7 mages totall s used as the pre-defned canoncal mage. RGC: Regon-based GC. ts canoncal mage s the same as for GC; GC+RHE: perform RHE after GC; RGC+RHE: perform RHE after RGC; RHE+RGC: perform RGC after RHE; HE+RGC: perform RGC after HE; QR: Quotent llumnaton Relghtng. For each person, the mage captured under the frontal lght source (A+E+) s chosen as the normal lght mode. he Quotent llumnatons for the remanng 63 non-frontal lghtng modes aganst the normal one for each person are computed accordng to the Leave-one-out strategy,.e., when computng the 63 quotent llumnatons for one person, only the remanng nne persons mages are used as the Quotent mage Bootstrap Set. he effect of these processng methods s llustrated n Fgure 4 (and Fgure 7), from whch ntutve effect can be observed for ther performance aganst extreme lghtng condtons. k k Subset 1. Subset 2. Subset 3. Subset 4. Subset 5. Fgure 3. Example mages of an ndvdual l n frontal pose from the Yale face database B wth the varablty due to llumnaton. Orgnal HE GC RHE RGC GC+RHE RGC+RHE QR Fgure 4. he processed mages after dfferent llumnaton on normalzaton methods for one mage n the Yale face database B. he acronym label below each mage shows the processng method. Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

6 All these methods are then compared by recognton experments. n all the experments, the Subset 1 (7 mages for each person) s chosen as the gallery and each of the mages n the remanng 4 subsets are matched n the gallery to fnd a nearest neghbor based on cosne smlarty. he expermental results are llustrated n able 1 and Fgure 5. able 1. Recognton rate comparsons of dfferent llumnaton normalzaton methods on Yale Face Database B (Subset 1 contanng 7 mages s used as the gallery for each person) Subset No. (otal Number of probes*) Methods 2 (118) 3 (118) 4 (138) 5 (189) Mean (563) Non GC RGC HE RHE RHE+RGC HE+RGC GC+RHE RGC+RHE QR *Note: otally 7 mages n our verson of the face database are absent. Harvard face database, whch s also specalzed on llumnaton. n each mage n the database, one subect held hs/her head steady whle beng llumnated by a domnant lght source. he space of lght source drectons, whch can be parameterzed by sphercal angles, was then sampled n 15 o ncrements. n the database, there are totally 66 mages of totally 1 subects, and they are dvded nto 5 subsets accordng to the greater of the longtudnal and lattudnal angles of the lght source drecton from the camera axs Subset 1(15 o ), Subset 2(3 o ), Subset 3(45 o ), Subset 4(6 o ), and Subset 5(75 o ). See [6] for detals. Subset 1 Subset Non HE RHE GC RGC RHE+RGC HE+RGC GC+RHE RGC+RHE QR Subset 2 Subset 3 Subset 4 Subset 5 Average Fgure 5 Recognton rate comparsons of dfferent llumnaton normalzaton methods on Yale Face Database B Note that these experments are all the nterpolaton of the varyng llumnaton snce subset 1 contans faces lghted under lght sources wth smaller angles, whle those of the mages n the other subsets are much greater. 4.3 Results on Harvard face database o further verfy the expermental results on the Yale face database B, smlar experments are organzed on the Subset 3 Subset 4 Subset 5 Fgure 6. Example mages of an ndvdual n frontal pose from the Harvard face database wth the varablty due to llumnaton. Smlar experments as n secton 4.2 are then conducted. he tested llumnaton normalzaton methods are all the same as n secton 4.2. he effect of these processng methods s llustrated n Fgure 7. All these Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

7 methods are then compared by recognton experments. n all the experments, the Subset 1 (6 mages for each person) s chosen as the gallery, and each of the mages n the remanng 4 subsets are matched n the gallery to fnd a nearest neghbor based on cosne smlarty. he expermental results are llustrated n able 2 and Fgure 8. Orgnal HE GC Non HE RHE GC RGC RHE+RGC HE+RGC GC+RHE RGC+RHE QR Subset 2 Subset 3 Subset 4 Subset 5 Average Fgure 8 Recognton rate comparsons of dfferent llumnaton on normalzaton methods on Harvard Face Database. RHE RGC HE+RGC 5. Conclusons and Future Work RHE+RGC RGC+RHE QR Fgure 7. he processed mages after dfferent llumnaton normalzaton methods for one mage n the Harvard face database. he acronym label below each mage shows the processng method. able 2. Recognton rate comparsons of dfferent llumnaton normalzaton methods on Harvard Face Database. For each methods, subset 1 contanng 6 mages s used as the gallery for each person. Subset No. (otal Number of Probes*) 2 (9) 3 (13) 4 (17) 5 (21) Mean (591) Non GC HE RGC RHE RHE+RGC HE+RGC GC+RHE RGC+RHE QR *Note: otally 9 mages n our verson of the face database are absent. he expermental results both on Yale and Harvard face database n Secton 4 reveal a number of nterestng ponts: (1) Smple llumnaton normalzaton method, e.g. the HE or the proposed GC can generally mprove the recognton performance compared wth the nonpreprocessng case; (2) Regon-based HE and/or GC can sgnfcantly mprove the recognton rate compared wth the nonpreprocessng case snce t can elmnate the heavy sde lghtng effects. (3) f the lght mode of the nput mage s known or can be estmated, the proposed QR methods can further consderably mprove the performance of the recognton system even compared wth the Regonbased HE combnng GC. Note that the terrfc performance of QR s based on the assumpton that the lghtng modes of the mages are known or can be estmated. hs s a strong constrant n a practcal applcaton system. herefore, one of our future works wll be the clusterng and classfcaton (estmaton) of the lghtng condtons for a practcal QR method. n contrast, the RHE combed wth RGC methods are more general and practcal to be exploted n a recognton system effcently, snce they need not the llumnaton estmaton procedure. Acknowledgement hs research s partly sponsored by Natonal H-ech Program of Chna (No.21AA11419 and No. 22AA1181), SVSON ech. Co., Ltd. And the authors would also gve thanks to those who provded the publc face databases. Proceedngs of the EEE nternatonal Workshop on Analyss and Modelng of Faces and Gestures (AMFG 3) /3 $ EEE

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