Avatar Face Recognition using Wavelet Transform and Hierarchical Multi-scale LBP

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1 th Internatonal Conferene on Mahne Learnng and Applatons Avatar Fae Reognton usng Wavelet Transform and Herarhal Mult-sale LBP Abdallah A. Mohamed, Darryl D Souza, Naouel Bal and Roman V. Yampolsky Computer Engneerng and Computer Sene Unversty of Lousvlle Lousvlle, USA {aamoha04, darryl.dsouza, n0bal02, roman.yampolsky}@lousvlle.edu Abstrat Reognzng avatars n vrtual worlds s a very mportant ssue for law enforement agenes, terrorsm and seurty experts. In ths paper, a novel fae reognton tehnque based on wavelet transform and Herarhal Mult-sale Loal Bnary Pattern (HMLBP) s presented and shown to nrease the auray of reognton of avatar faes. The proposed tehnque onssts of three stages: preproessng, feature extraton and reognton. In the preproessng and feature extraton stages, the wavelet deomposton s used to enhane the ommon features of the same lass of mages and the HMLBP s used to extrat representatve features from eah avatar fae mage wthout a need for any tranng. In the reognton stage, the Ch- Square dstane s used to aheve a robust deson and to ndate the orret lass to whh the nput mage belongs. Experments onduted on two manually ropped avatar mage datasets (Seond Lfe and Entropa Unverse) show that the proposed tehnque performs better than tradtonal (sngle sale) LBP, Wavelet Loal Bnary Pattern (WLBP) and HMLBP n terms of auray (78.57% and 67.50% reognton rates for Seond Lfe and Entropa Unverse datasets respetvely). Keywords-Avatar; fae reognton; HMLBP; LBP; Seond Lfe; wavelet transform I. INTRODUCTION A vrtual world s a three-dmensonal, omputer-based smulated envronment omprsed of onlne ommuntes onneted over the Internet. These worlds are rapdly ganng momentum as they possess the apablty to enrh soety. They are ganng popularty aross the globe and are movng towards beng an ntegral part of t. Seond Lfe [6], Atve Worlds [7], and Entropa Unverse [8] are a few popular vrtual worlds wth mllons of regstered users. Vrtual worlds brng a sense of a personal dgtal spae for users by mrrorng real world atvtes. The users reate ther own avatars and navgate the world. An avatar s the user s vrtual dentty wthn these worlds whose appearane an be altered as per the user s hoe. Ths provdes them wth a lot of flexblty and adaptablty. Communtes, soal groups, enterprses and nsttutons are all present wthn the vrtual worlds. An avatar an navgate the world by movng around buldngs, fly and swm as well as teleport to dfferent loatons. Communaton between users ranges from text, vsual gestures, sound and oasonally touh and voeommands. Vrtual money whh an be purhased usng real money ould be exhanged for goods and serves. Gven the omplex nature of these adaptable and personal worlds they possess potental for dong a lot of good or bad thngs. Eduaton s fostered wth a strong emphass on teahng and learnng. Interatvty and ollaboraton between users helps aheve tasks and bond people together. However, destrutve purposes nvolvng tradtonal rmes lke dentty thefts, fraud, tax evason, llegal gamblng and terrorst atvtes are reportedly on the rse n vrtual worlds [9]. Quk nvestgaton of Seond Lfe reveals that t s populated by numerous terrorst organzatons assoated wth Al-Qaeda who an tran n suh smulated envronments usng weapons dental to ther real-world ounterparts [10]. These rmnal atvtes are posng a grave problem to the law enforement agenes n these lawless vrtual worlds. Forens experts are expressng nterest n aurate and automat trakng of users and ther avatars. Authentatng humans (bologal enttes) s essental and a well-developed sene utlzed to determne one s dentty n today s modern soety. However, avatar authentaton (non-bologal enttes) s an ssue that needs to be hghlghted and addressed [11]. Proflng avatars s a novel and a hallengng approah ontrbutng towards a new researh dreton n fae deteton and reognton. To address the onerns for an affordable, automat, fast, seure, relable and aurate means of dentty authentaton Yampolsky et al. defne the onept of Artmetrs a feld of study that wll allow dentfyng, lassfyng and authentatng robots, software and vrtual realty agents [12, 13]. In the ontext of nvestgatng rmnal and terrorst atvty n vrtual worlds four senaros requrng a fae reognton algorthm [9]: a. Mathng a Human fae to an Avatar fae Generally many users have the tendeny to use ther real fae as ther onlne avatar whh helps represent them well. b. Mathng one avatar fae wth another Ths apablty helps to ontnuously trak an avatar through yberspae at dfferent plaes at dfferent tmes.. Mathng an Avatar s fae from one vrtual world to the same avatar n a dfferent vrtual world A reent development wthn vrtual ommuntes s to nteronnet dfferent vrtual worlds. Ths wll help n unquely dentfyng and trakng reords of the avatars. d. Mathng an Avatar sketh to the Avatar fae Just lke the tradtonal methods of mathng the forens sketh of human faes provded by the desrpton of the vtm or wtness to ther real faes, t s equally mportant /11 $ IEEE DOI /ICMLA

2 to map ths sheme wthn vrtual worlds to math the vrtual rmnal wth ts avatar dentty. Pror work on authentaton of avatars s lmted. Klare et al. [9] emphasze the sgnfane of fae reognton wthn vrtual worlds. An approah for parameterzed generaton of avatar fae datasets was reported n [14]. The faal bometr authentaton of avatars usng wavelet transform for feature extraton and SVM for lassfaton s dsussed n [11]. Yampolsky & Gavrlova have appled bometr prnples towards avatar reognton and outlned future dretons and potental applatons [12]. Boukhrs et al. [15] have presented an approah for applyng fae reognton to avatars as part of seurty framework for vrtual worlds. Mohamed & Yampolsky have appled wavelet transform wth Loal Bnary Pattern (LBP) to reognze avatar faes [16]. In ths paper, we propose a new avatar fae reognton algorthm by usng the dea of wavelet deomposton n preproessng the avatar fae mages and then extratng the fae features usng Herarhal Mult-sale Loal Bnary Pattern (HMLBP). The expermental results demonstrate the effeny of the proposed algorthm. The remander of ths paper s organzed as follows: In Seton 2 we provde an ntroduton to Wavelet transformaton, ts role and benefts n the feld of mage proessng. Seton 3 desrbes the LBP operator and hstogram. Seton 4 explans the wavelet herarhal multsale LBP (the proposed algorthm). Experments mplemented on avatar fae datasets are presented n seton 5. The omparsons of the proposed algorthm wth varous other methods are also gven n Seton 5. Fnally, useful onlusons are gven n seton 6. II. WAVELET DECOMPOSITION OF AN IMAGE Wavelet Transform (WT) or Dsrete Wavelet Transform (DWT) s a very popular tool for mage analyss n the feld of mage proessng. It helps to vew and proess dgtal mages at multple resolutons. Its mathematal bakground and advantages have been dsussed n many researh artles [17]. The hef advantages of usng Wavelet Transforms are lsted below: It deomposes an mage by redung the resolutons of ts sub-mages and helps redue the omputatonal omplexty of the system. Harmon [18] demonstrated that the mage wth 16 x 16 resoluton s suffent to reognze human faes. It deomposes mages nto sub-bands orrespondng to dfferent frequeny ranges. They easly meet the nput requrements for the next major step thus, mnmzng the omputatonal overhead n the proposed system. Wavelet deomposton provdes loal nformaton n both spatal and frequeny domans, n omparson wth Fourer deomposton, whh supports only global nformaton n the frequeny doman [19]. Thus, they provde spatal and frequeny haratersts of the mage at the same tme. The man haraterst of wavelets s that they provde mult-resoluton analyss of the mage n the form of oeffent matres. Strong arguments for the use of ths multresoluton deomposton n psyhovsual researh support evdene that humans proess mages n a mult-sale way. The omputatonal omplexty of wavelets s lnear wth the number (N) of omputed oeffents (O(N)), whle other transformatons n ther fast mplementaton have a N log 2 (N) omplexty. Thus, wavelets are adapted towards dedated hardware desgns. The bas funtons of wavelet transform are obtaned from a sngle prototype (mother) wavelet by dlaton and translaton. The mother wavelet funton for the 1-D sgnal f(t) s shown below: 1 t n ψ m, n ( t) = ψ ( ) (1) m m Ths equaton [17] an be dsretzed by restranng m and n to a dsrete latte where m=2 s and n Ζ wth s beng the sale. The mother wavelet has to satsfy the admssblty rteron to ensure t s a loalzed zero-mean funton. Typally, some more onstrants are mposed on to ensure the transform s non-redundant, omplete and onsttutes a mult-resoluton representaton of the orgnal sgnal. Fg. 1 gves an dea about the struture of the wavelet oeffent and the frst and the seond level of wavelet deomposton for one of the avatar fae mages used n the experments. The two-dmensonal wavelet transform s performed by applyng a one-dmensonal wavelet transform to the rows and olumns of the two-dmensonal data. The one-level wavelet deomposton of an mage resulted n an approxmaton mage (LL 1 ) and three detal mages n horzontal (HL 1 ), vertal (LH 1 ) and dagonal (HH 1 ) dretons respetvely. The approxmaton mage, obtaned by a low-pass flter, (a) () (d) Fgure 1. (a) Wavelet oeffent struture [1-3] (b) A sample mage of one of the avatar fae mages n the dataset () One level wavelet deomposton for the avatar fae mage n b (d) Two levels wavelet deomposton for the avatar fae mage n b. (b) 195

3 ontans the low-frequeny nformaton of the fae mage and t s used for the next level of deomposton. The detal mages ontan most of the hgh frequeny nformaton of the fae mage suh as llumnaton and faal expressons whh are alled the loal hanges of the fae mage [20]. The orgnal mage s thus represented by a set of sub-mages at several sales [17]. III. LOCAL BINARY PATTERN The loal bnary pattern (LBP) operator, ntrodued by Ojala et al. [21], s a powerful loal desrptor for desrbng mage texture and has been used n many applatons suh as ndustral vsual nspeton, mage retreval, automat fae reognton and deteton. The LBP operator labels the pxels of an mage by thresholdng the value of the entral pxel aganst ts surroundng 8 pxels (for a gven sze of 3x3 neghborhood of eah pxel) and onsderng the result as a bnary value [22]. The bnary value wll be onverted to the demal value to get the LBP value. The output value of the LBP operator an be defned as follows [5, 22]: 7 LBP ( x, y ) = 2 S ( g g ) (2) = 0 where g orresponds to the gray value of the entral pxel, (x, y ) are ts oordnates, g ( = 0,1,2,..,7) are the gray values of ts surroundng 8 pxels and S(g - g ) an be defned as follows: 1, g g S ( g g ) = (3) 0, otherwse So we an say that LBP s an ordered set of bnary omparsons between the entral pxel value and the values of ts neghborhood pxels [5]. Fg. 2 gves an llustraton of the bas LBP operator and how to ompute the LBP value. The LBP operator an be extended to use pxels from neghborhoods of dfferent szes [3, 5, 20]. Fg. 3 gves us some examples of dfferent LBP operators where R s the radus of the neghborhood and P s the number of pxels n that neghborhood. The neghborhood an be ether n a rular or square order. Usng the rular order neghborhood allows any radus and number of the pxels n the neghborhood [23]. One of the most mportant and suessful extensons to the bas LBP operator s alled unform LBP (ULBP). An LBP s alled unform when t ontans at most two dfferent onversons from 0 to 1 or 1 to 0 when the bnary strng s (P=8, R=1) (P=8, R=1.5) (P=8, R=2) Fgure 3. Three dfferent LBP operators [3] vewed as a rular bt strng [3, 23]. For example, , and are unform patterns. Ojala reported that wth P = 8 and R = 1 neghborhood, unform patterns aount for around 90% of all patterns and wth P =16 and R = 2 neghborhood, unform patterns aount for around 70% of all patterns [3]. So only a lttle amount of nformaton wll be lost when usng unform patterns [5]. After labelng an mage usng the LBP operator, the hstogram of the labeled mage an be defned as follows [5]: H = I ( f ( x, y) = ), = 0,1,.., n 1 x, y where n s the number of dfferent labels produed by the LBP operator, f(x, y) s the labeled mage and I (A) s a deson funton wth value 1 f the event A s true and 0 otherwse. LBP hstogram has very useful nformaton about the dstrbuton of the loal mrostrutures, suh as spots and edges, over the whole mage and so an be used to desrbe and represent the global haratersts of the mage [5, 20]. IV. WAVELET HIERARCHICAL MULTI-SCALE LBP (WHMLBP) The proposed algorthm has three steps: preproessng, feature extraton and reognton or lassfaton. A. Preproessng Fae Image To mprove the effeny of extratng the fae features we have to apply a set of preproessng operatons. Frst, we manually ropped the nput mages to pure fae mages by removng the bakground whh s not useful n reognton. Seond, these pure fae mages have to be normalzed and then deomposed usng the frst level of wavelet deomposton to obtan pure faal expresson mages (See Fg. 4). Detaled mages resultng from applyng wavelet deomposton ontan hanges whh represent the dfferene of fae mages. So onsderng only the approxmaton mages wll enhane the ommon features of the same lass of mages and at the same tme the dfferene wll be redued. For ths reason, our experments were onerned only wth the approxmaton mages resultng from the frst level of wavelet (4) Fgure 2. The bas LBP operator [3, 5] Orgnal mage Cropped mage Normalzed Frst level Cropped mage deomposton Fgure 4. Fae mage preproessng 196

4 deomposton and whh we used n testng to evaluate the performane of the proposed algorthm. B. HMLBP Feature Extraton The performane of the mult-sale or mult-resoluton LBP operator s better than the performane of a sngle sale LBP operator for many reasons, suh as: a- Mult-sale operator an help to extrat more mage features under dfferent settngs [4]. Calulatng features based on a lmted sze neghborhood n sngle sale LBP may lead to nadequate apture of domnant features of an mage. b- As a result of sngle sale LBP operator non-unform patterns are lustered nto one non-unform pattern. As the radus of the LBP nreases, the luster sze of the non-unform patterns nreases as well, leadng to a substantal loss of nformaton [4]. Some work [4] was arred out towards extratng more useful features from the mage by dggng out nformaton from the non-unform patterns. Suh methods are based on a tranng step to learn the useful patterns and so the tranng samples have a great effet on the auray of reognton [4]. In HMLBP algorthm the LBPs for the bggest radus s extrated frst. The new LBPs of non-unform patterns have to be extrated further usng a smaller radus to extrat unform patterns. Ths proess ontnues untl the smallest radus s proessed. Ths herarhal sheme does not have a tranng step and thus t s nsenstve to tranng samples [4]. Fg. 5 shows an example of the herarhal mult-sale LBP sheme. The LBP hstogram for R=3 s frst bult. For those non-unform patterns of the R=3 operator, a new hstogram s bult by the R=2 operator. Then, the nonunform patterns of R=2 lead to the hstogram buldng proess for the R=1 operator. Fnally the three hstograms are onatenated nto one mult-sale hstogram to form the feature hstogram of an mage [4]. C. Dssmlarty Measure The last stage of our proposed algorthm s to lassfy eah faal mage to ts lass by omputng the dssmlarty between tranng samples and a test (nput) sample. To do that we apply Ch-Square dstane as follows [3]: N 2 ( X n Yn ) D( X, y) = (5) n= 1 X n + Yn where X s the tested mage (sample), Y s the tranng sample(s) or mage(s) and N s the sum dmenson. V. EXPERIMENTAL RESULTS AND ANALYSIS To ensure the effeny of the proposed method, two vrtual world datasets are used to test the performane of the proposed method. Ths s the frst tme gven algorthm s used on the gray sale mages and onsequently there s no baselne results avalable for dret omparson. The frst dataset, from Seond Lfe vrtual world, ontans 581 (1280 x 1024 pxels) gray sale mages of 83 avatars. The seond dataset, from Entropa vrtual world, onssts of a total of 490 (407 x 549 pxels) gray sale mages representng 98 avatars. We tested these datasets wth three well-known algorthms (LBP, WLBP and HMLBP) and ompared ther result wth the results omng from the proposed method. A. Expermental setup All mages n the Seond Lfe dataset are manually ropped to 260x260 pxels whle mages n Entropa dataset are manually ropped and reszed to 180x180 pxels. The resulted 581 Seond Lfe avatar fae mages dataset s organzed nto 83 lasses eah of whh has 7 fae mages of the same avatar wth dfferent frontal angles (front, far left, md left, far rght, md rght, top and bottom). So we an say that the Seond Lfe avatar fae mages dataset fouses on pose angle and faal expresson. The resulted Entropa avatar fae dataset s organzed nto 98 lasses eah of whh has 5 avatar fae mages. In one of them the avatar s wearng a mask whle n the others the avatar has dfferent faal expressons and eye angles. See Fg. 6 for an example of two lasses of avatars (one from eah dataset) before and after roppng. The resoluton of mages used n the experments s hanged from 260x260 to 130x130 pxels (for Seond Lfe dataset) and from 180x180 to 90x90 (for Entropa dataset) usng the frst level of wavelet deomposton. The avatar fae mages n both datasets are preproessed and prepared for feature (a) Fgure 5. An example of herarhal mult-sale LBP Sheme [4] (b) Fgure 6. a. Two lasses of unproessed avatar mages. b. The same two lasses after roppng the avatar faes. 197

5 extraton step. HMLBP s used to extrat the best desrptve features and then at the end the Ch-Square measure s appled to aomplsh lassfaton. The experments are performed on the ondton of a sngle tranng mage. Eah tme one mage s used as a traner. The Ch-Square dstane omputes the dssmlarty between ths mage and all other mages n the dataset. These dstanes wll then be ordered n an asendng order. The 6 mages (for Seond Lfe dataset) assoated to the least 6 dstanes n the asendng order wll be heked f they are from the same lass of the traned mage or not. The same wll be done but wth only 4 mages for the Entropa dataset. Based on the number of orreted lassfed mages we an ompute the auray for eah dataset usng the followng formula: lassfaton auray (CA) or reognton rate (RR) equaton: number of orreted lassfed mages RR = x100 % total number of samples n the dataset B. Comparng WHMLBP wth HMLBP and other algorthms In order to gan better understandng on whether usng wavelet transform wth HMLBP s advantageous or not we ompared WHMLBP wth HMLBP, WLBP and LBP wth several experments. Frst we got the performane of WHMLBP wth dfferent blok sze wth R = [3, 2, 1] and P = [16, 16, 16] as we an see n Fg. 7. We an see that hangng the blok sze affets the result of the reognton rate. In Fg. 7, the reognton rate s nreased as the blok sze s larger, and the performane s dropped as the blok sze s larger than 42x42 on the two datasets, that s beause dense bloks obsure the mage features. As a result we ompare the performane of WHMLBP and HMLBP usng 42x42 blok sze wth the same radus R = [3, 2, 1] and a dfferent neghborhood sze for the two datasets as n Fg. 8. The expermental results showed that the reognton rate of WHMLBP nreases about 4% to 5% n Seond Lfe dataset and the greatest auray s about 80.03% when the neghborhood sze s 24*24*24. And n the Entropa dataset, almost all the ases are better than usng HMLBP whle the auray rate nreases about 1%. The average of the reognton rate of the two methods for both datasets usng dfferent neghborhood szes an be seen n table I. To ompare the performane of WHMLBP method wth other methods, we appled WLBP and LBP methods on the same two datasets. We appled both methods wth R = 1, 2, 3 and P = 8, 16, 24 and we got the average of the reognton Table I. Average Reognton rate for dfferent algorthms Dataset LBP WLBP HMLBP WHMLBP Seond Lfe 67.42% 77.27% 74.34% 78.57% rate for both datasets as n table I. The results we obtaned demonstrate the effetveness of our algorthm n omparson to other algorthms. VI. CONCLUSION In ths paper, to mprove the effeny of the HMLBP n extratng useful features from an mage we appled wavelet transform to the normalzed manually ropped mages. The effetveness of ths proposed method s shown n two avatar fae datasets. Compared wth HMLBP method, the proposed method gets more than 4% mprovement n the frst dataset and about 1% mprovement n the seond one. Compared wth two other well-known methods (LBP and WLBP) the proposed method gets hgher reognton rate. Applyng other lassfers may lead to better results and ths s what we ntend to attempt n the future usng larger datasets from dfferent vrtual worlds. The fnal goal s to buld a omplete automat system for avatar fae deteton and reognton. (a) (b) Fgure 8. The RR of WHMLBP and HMLBP on: (a) Entropa dataset (b) Seond Lfe dataset. Entropa 66.45% 65.78% 66.90% 67.50% Fgure 7. Performane of WHMLBP wth dfferent blok sze. REFERENCES [1] Wang, H., S. Yang, and W. Lao, An mproved PCA fae reognton algorthm based on the dsrete wavelet transform 198

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