Face Recognition Using Ada-Boosted Gabor Features

Transcription

1 Face Recognton Usng Ada-Boosted Gabor Features Peng Yang, Shguang Shan, Wen Gao, Stan Z. L, Dong Zhang Insttute of Coputng Technology of Chnese Acadey Scence Mcrosoft Research Asa {pyang, Abstract Face representaton based on Gabor features has attracted uch attenton and acheved great success n face recognton area for the advantages of the Gabor features. However, Gabor features currently adopted by ost systes are redundant and too hgh densonal. In ths paper, we propose a face recognton ethod usng ed Gabor features, whch are not only low densonal but also dscrnant. The an contrbuton of the paper les n two ponts: ) s successfully appled to face recognton by ntroducng the ntra-face and extra-face dfference space n the Gabor feature space; ) An approprate re-saplng schee s adopted to deal wth the balance between the aount of the postve saples and that of the negatve saples. By usng the proposed ethod, only hundreds of Gabor features are selected. Experents on FERET database have shown that these hundreds of Gabor features are enough to acheve good perforance coparable to that of ethods usng the coplete set of Gabor features.. Introducton Face recognton has a varety of potental applcatons n publc securty, law enforceent and coerce such as ug-shot database atchng, dentty authentcaton for credt card or drver lcense, access control, nforaton securty and vdeo survellance. In addton, there are any eergng felds that can beneft fro face recognton, such as huan-coputer nterfaces and e-servces, ncludng e-hoe, tele-shoppng and tele-bankng. Related research actvtes have sgnfcantly ncreased over the past few years []. The ost popular extng technologes for face recognton nclude Egenface PCA) [], FsherFace [3], Independent Coponent Analyss ICA) [4], Bayesan face recognton [5] and Elastc Bunch Graph Matchng EBGM) [7]. In the FERET test [6], Fsherface, Bayesan atchng and EBGM were aong the best perforers. Especally, the EBGM has attracted uch attenton because t frstly exploted the Gabor transfor to odel the local features of faces. However, EBGM takes the coplete set of Gabor features, ost of whch are redundant for classfcaton. For exaples, Fasel has ponted out n [8] that the Gabor features used n [7] are not the best ones for the detecton of facal landarks. However, no ethod has been proposed on how to select the ost dscrnant Gabor features for recognton purpose. Ths paper s an attept to answer ths queston by ntroducng the ethod nto the Gabor feature-based face recognton ethod. Face recognton s a ult-class proble, therefore, n order to use for classfcaton, as n [5] and [9], we propose to tran based on the ntra-personal and extra-personal varaton n the Gabor feature space. Based on a large database of ages, selects a sall set of avalable Gabor features fro the extreely large set. The fnal strong classfer, whch cobnes a few hundreds of weak classfers Gabor features), can evaluate the slarty of two face ages. The flowchart of recognton process n our syste s as followng: Extractng Gabor features of age I Extractng Gabor features of age I Strong classfer learned by S,, the Slarty of age I and age I Fg.. The flowchart of the proposed face recognton ethod. A face recognton syste coprses two stages: tranng and testng. In practcal applcatons, the sall nuber of avalable tranng face ages and the coplcated facal varatons durng the testng stage are the ost dffcult probles for current face recognton

2 systes. Therefore, a lot of work has been done on tranng set, ncludng re-saplng, such as [9]. The reanng part of ths paper s organzed as follows: In secton, the Gabor representaton of face s ntroduced. Secton 3 presents the ntra-personal and extra-personal space. Secton 4 descrbes the boostng learnng for feature selecton and classfer constructon. The re-saplng schee we proposed s conducted n secton 5. Experents and analyss are conducted n secton 6, followed by a sall dscusson, concluson and future work n secton 7.. Gaborface Gabor flter can capture salent vsual propertes such as spatal localzaton, orentaton selectvty, and spatal frequency characterstcs. Consderng these excellent capactes and ts great success n face recognton [6], we choose Gabor features to represent the face age. Gabor flters are defned as follows: r k, / ) u v ku, v z σ ku, v z σ / ψ u, v z) = e [ e e ], ) σ φ k u ax where k = gves the frequency, k = k e u, v v ; v v f uπ φu = 8, φu [0, π ) gves the orentaton, and z = x, y). φu ku, v = kve, ) k, e z u v where s the oscllatory wave functon whose real part and agnary part are cosne functon and snusod functon respectvely. In equaton, v controls the scale of Gabor flters whch anly deternes the center of the Gabor flter n the frequency doan; u controls the orentaton of the Gabor flter. In our experent we use the Gabor flters wth the followng paraeters: fve scales v {0,,,3,4} and eght orentatons u {0,,,3,4,5,6,7} wthσ = π, k ax = π /, and f =. The sae paraeters are also taken n [7]. The Gaborface, representng one face age, s coputed by convolutng t wth correspondng Gabor flters. Fgure shows the Gaborface representaton of a face age. a) b) Fg.. Gaborface representaton for one face. The face age s represented by Gaborface, whch s used to construct the ntra-personal space and the extrapersonal space. The constructon process wll be ntroduced n the followng secton. 3. Intra-personal and Extra-personal Space In FERET96 test, the Bayesan ethod proposed by Moghadda and Pentland [5] was the top one perforer. Although n FERET97 test t was exceeded by the algorth of UMD Unversty of Maryland) [6], t has shown the strong potental n face recognton and other applcatons of pattern recognton, and has becoe one of the ost wdely nfluental face recognton algorths. In nature, the thought of the face recognton ethod of Moghadda and Pentland [5] s to convert the ult-class proble nto the two-class proble. Bascally, face recognton s a ult-class proble. Moghadda and Pentland [5] used a statstcal approach that learned the varatons n the dfferent ages of an ndvdual to for the ntra-personal space, and the varatons n the dfferent ages of dfferent ndvduals to for the extra-personal space. Therefore, the ult-class proble s converted nto a two-class proble. The estaton of the ntra-personal and the extra-personal dstrbutons s based on the assupton that the ntra-personal dstrbuton s Gaussan. In our syste, the defntons of the ntra-personal class and the extra-personal class are as follows: I,k s a face age, where the subscrpt eans ths age belongs to the ndvdual whose ID s ; I s a face age of another subect; GI eans the transfored ages got by convolutng I wth the Gabor flters; GI eans the transfored ages got by convolutng I wth the sae Gabor flters; H I I ) = GI GI eans the dfference of the two ages. If ntra-personal space. On the contrary, f =, H I I ) s n the, H I I ) s n the extra-personal space. In our syste, n the tranng process, f =, H I I ) s a postve exaple; otherwse, H I I ) s a negatve exaple. Fgure 3 shows soe dfferent ages n ntra-personal space and extra-personal space. In [5], Maxu a Posteror MAP) rule s taken to obtan the two probablstc slarty easures. Obvously, the ntra-personal and extra-personal proble s a twoclass proble. As we know, boostng learnng s a strong tool to solve two-class classfcaton probles. Notcng the great success of n face detecton area, we exploted t n our ethod to dstngush the ntra-personal space fro the extra-personal space. We use to select a sall set of Gabor features or weak classfers) fro the orgnal extreely hgh

3 densonal Gabor feature space to for a strong classfer, whch s used to calculate the slarty of a par of Gaborfaces. Equaton 3, a strong classfer learned by, s taken to easure ther slarty: where M S I, I ) = α h I, I ), 3) = a s the cobnng coeffcent and h I, I ) threshold functon. How to derve be dscussed n the followng secton. I, I a and ) s a h wll whether two dfferent face ages are fro the sae subect, naturally,, a verson of the boostng algorth, s taken to solve ths two-class proble. Therefore, we use to tran a strong classfer. The fraework of the tranng process of the proposed ethod s llustrated n fgure 4. Extractng Gabor feature of age I,k Extractng Gabor feature of age I,k =! = Postve saple set. All saples n ths set are labeled+ Negatve saple set. All saples n ths set are labeled- A strong classfer and the features selected Fg.4. Fraework of the proposed tranng process. A strong classfer s fored by, whch cobnes a nuber of weak classfers. The process s descrbed n Table. a) Intra-personal age b) Extra-personal age Fg.3. Intra-personal age and Extra-personal age represented by Gaborfaces. 4. Learnng the ost Dscrnant Gabor features by A large nuber of experental studes have shown that classfer cobnaton can explot the dscrnatng power of ndvdual feature sets and classfers. Wth the success of boostng n the applcaton of face detecton, boostng, as one of the ost coonly used ethods of cobnng classfers based on statstcal re-saplng technques, has shown strong ablty to resolve the two-class proble. For Intra-personal and Extra-personal s used to descrbe Table. The algorth for classfer learnng Gven labeled exaples Set S and ther ntal weghts ω Do for t=,, T:. Noralze the weght ω t. For each feature, k, tran a classfer h k wth respect to the weghted saples 3. Calculate error, choose the classfer h t wth the lowest error, getα t, the weght of h t. 4. Update weghts ω, t+ Get the strong classfer T Sx ) = α h x) of table s re-wrtten as equaton 3), t= t t T Sx ) = αtht x) S x ) = S I, I ) = α h I, I ) M t= =, where α 0 s the cobnng coeffcent whch s used to descrbe the slarty of I and I on feature. Therefore, S I, I ) s used to evaluate the slarty of age I and age I on the selected features. 5. Re-saplng fro the large pool of extraperson dfference Gven a tranng set that ncludes N ages for each of the K ndvduals, the total nuber of age pars s KN. A

4 sall norty, N K, of these pars are fro the sae ndvdual. Any approach for learnng the slarty functon should explctly handle the proble of how to choose lted saples fro the overwhelngly large nuber of negatve saples to deal wth the treendous balance of the postve and the negatve saples. A sple proposal to solve ths proble s to take a rando subset of these pars for tranng, but t can not ensure that the rando subset could represent all the saples actually, so the re-saplng schee we proposed s taken to guarantee that all possble saples can be referred durng tranng. Fgure 5 s the flowchart of the tranng procedure, n whch S s a strong classfer boosted by weak classfers whch are learned fro the current tranng set n the th stage; T s the threshold tll? the th stage, whch ensures to get the false postve and the detecton rates that we need; and R s the re-saplng operaton after the th stage. S Resaplng S S Resaplng S+S Fg.5. The flowchart of re-saplng procedure. S3 Further Processng The rato of postve saples to negatve saples s balanced, snce the nuber of negatve saples s grossly larger than that of the postve saples. In the tranng set, the rato of postve saples to negatve saples s kept :7. How to re-saplng s a key of our syste, t wll be ntroduced n the followng. Because of the balanced rate of postve saples to negatve saples, all postve saples are reserved n each stage and the negatve saples are selected by re-saplng after each stage. Dfferent fro the face detecton [], each stage n our syste has a false postve rate of about 0.0, whch ensures that the weak classfers learned n ths stage are wholly capable of separatng the postve saples fro the negatve saples. Although we can use the copletely sae steps as [] to tran a cascade of classfers, the result of t s not as good as the strategy we take n followng steps. And ths wll be further proved by the coparson experents n secton 6. In [], after tranng a stage, re-saplng s also used to select saples. If a negatve saple x could pass all of the stages whch have been traned, x s selected. In our strategy, x, a negatve saple, does not need to pass all of the stages one by one; t ust needs to pass the strong. So soe negatve saples classfer S, f Sx ) T traned n prevous stages aybe reoccur n the latter stages. Table. Tranng process wth re-saplng schee we proposed Gven labeled exaples Set, nclude all postve saples and select negatve saples randoly at the rate of :7 fro whole negatve set. Do for t=,,t:.. S = S, t = 3. Select x randoly fro negatve set, f Sx ) T, add t to the new negatve set for the next round, and Sx) s kept n next stage to get proper threshold T +. Get a strong classfer S = S 6. Experent and Analyss We tested the proposed ethod on the FERET face database, and the tranng set s also fro the tranng set of FERET database, whch ncludes 00 ages of 49 subects. All ages are cropped and rectfed accordng to the anually located eye postons suppled wth the FERET data. The noralzed ages are 45 pxels hgh by 36 pxels wde. The tranng set yelds 795 ntra-face age pars and 500,706 extra-face age pars. At any te, all 795 ntra-face pars and 5000 extra-face pars are used for tranng. A new set of 5000 extra-face pars s selected fro the full tranng set by re-saplng schee we proposed after one stage of has fnshed. The nuber of Gabor features of each saple s = 64800, fro whch the tranng algorth would select hundreds of the ost dscrnant ones. We run n 7 stages, a total of 08 rounds, and got 08 features. The frst four features learned by our algorth are shown n fgure 6, fro whch one can fnd that they are all as ntutvely reasonable as the ost dscrnant Gabor features. = Fg.6. The frst four Gabor features selected by the proposed ethod. The experental relatonshp between the rank- recognton rates and the nuber of weak classfers s drawn n Fgure 7, whch s the result when testng the proposed ethod on the probe set FB and the gallery set T

5 FA of the FERET database. There are 96 ages n FA, 95 ages n FB, and all of the subects have exactly one age n both FA and FB. As t can be seen fro Fgure.7, wth the ncrease of the selected Gabor features, the rank- recognton rate proves fro 37.5% wth 6 features selected to 95.% wth 700 features selected. Wth ore features exploted, the perforance does not prove any longer. The result s coparable wth the reported best result on ths set n [6]. We also draw n Fgure 8 the cuulatve atch score curve of the proposed ethod on FB probe set aganst FA gallery. Fg.7. Face recognton perforance of the proposed ethod wth respect to the nuber of weak classfers. Fg.8. The Cuulatve atch score of the proposed ethod when testng on FERET FB probe set. To prove the advantage of the re-saplng ethod, all 795 ntra-face pars and 5000 extra-face pars randoly selected fro 500,706 extra-face age pars are used for tranng wthout re-saplng strategy. It eans that we run ust one stage of, a total of 000 rounds, and got 000 features. The sae test experent s done on FB and FA of the FERET database. Fgure 9 shows that the rank- recognton rate rases to 9.8% wth 74 features. The rank- recognton rate s ust 90.6% wth 700 features. Coparng the perforance of re-saplng ethod and none re-saplng ethod, we can draw ths concluson that the re-saplng strategy we proposed s effectve. Fg.9. Face recognton perforance of the ethod wthout re-saplng wth respect to the nuber of weak classfers 7. Concluson In the past few years, face representaton based on Gabor features has attracted uch attenton and acheved great success n face recognton area for several advantages of the Gabor flters ncludng ther localzablty, orentaton selectvty, and spatal frequency characterstcs. However, Gabor features currently adopted by ost systes are too hgh densonal to be used soothly n a practcal syste. Ths paper proposes to tackle ths proble by applyng the learnng approach. And a face recognton ethod usng ed Gabor features s proposed. ed Gabor features are not only low densonal but also dscrnant. To apply the successfully to face recognton proble, we ntroduce the ntra-face and extra-face dfference space n the Gabor feature space to convert the ult-class face recognton proble nto a two-class proble. In addton, to deal wth the balance between the aount of the postve saples and that of the negatve saples, a re-saplng schee s adopted to choose the negatve saples. By usng the proposed ethod, only hundreds of Gabor features are selected for classfcaton purpose. The experents on FERET database have shown that these hundreds of Gabor features are enough to acheve good perforance coparable to those ethods usng the coplete set of Gabor features, whch has pressvely shown the effectveness of the proposed ethod. Acknowledge Ths research s partally sponsored by NSFC under contract No , Natonal H-Tech Progra of Chna No.00AA490 and No. 00AA800). Ths work s also partally sponsored by ISVISION Technologes Co., Ltd.

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