Face Detection and Tracking in Video Sequence using Fuzzy Geometric Face Model and Mean Shift

Transcription

1 Internatonal Journal of Advanced Trends n Coputer Scence and Engneerng, Vol., No.1, Pages : (013) Specal Issue of ICACSE Held on 7-8 January, 013 n Lords Insttute of Engneerng and Technology, Hyderabad Face Detecton and Trackng n Vdeo Sequence usng Fuzzy Geoetrc Face Model and Mean Shft P. S. Hreath, Manjunath Hreath, Mahesh R. Departent of Coputer Scence Gulbarga Unversty, Gulbarga Karnataka, Inda hreathps53@yahoo.co, anju.gtl@gal.co, aheshsway99@gal.co Abstract : Huans ake use of face as an portant cue for dentfyng people. Ths akes autoatc face detecton very crucal fro the pont of vew of a wde range of coercal and law enforceent applcatons. Whle tradtonal face detecton s typcally based on stll ages, face detecton and trackng fro vdeo sequences has becoe pronent research doan. In ths paper, we propose a novel algorth whch segents the face regon n vdeo ages usng fuzzy geoetrc face odel n key frae. The ean shft s used to track the face along the vdeo sequence. Contrary to current technques that are based on huge learnng databases and coplex algorths to get generc face odels, the proposed ethod handles sple face detecton and trackng approach. The proposed ethod s pleented and evaluated wth nuerous experents on vdeos contanng large varatons of head oton, lght condton, and expressons. The experental results show that the proposed ethod s effectve n detectng and trackng faces n vdeos. Key words : Face detecton, fuzzy geoetrc face odel, ean shft, face trackng. INTRODUCTION Face detecton s requred as the frst step of the autoatc face age analyss syste. Face detecton syste has been wdely nvestgated n recent years because t overles any areas of applcaton such as face recognton, an-achne nteracton systes, vsual councaton systes, vdeo-survellance, etc. However, face detecton s a challengng task due to varaton n llunaton, scale, locaton, orentaton and pose. Many ethods for face detecton n stll ages and n vdeo sequences have been reported n the lterature, whch have acheved soe encouragng results. A coprehensve survey on ethods of face detecton n ages can be found n [1-6]. In general, face detecton technques can be dvded nto two categores: odel-based technque and feature-based technque. The frst one assues that a face can be represented as a whole unt. Several statstcal learnng echanss are explored to characterze face patterns, such as neural network, Bayesan classfer and boostng algorth. The second category consders a face as a collecton of coponents. Iportant facal features such as eyes, nose and outh are extracted fro face age, and by usng ther locatons and relatonshps, the faces n age are detected. Aong feature-based face detecton ethods, usng skn color as a detecton cue s very popular. Skn color s portant and powerful nforaton for huan face. Identfcaton of skn regon n an age can be used as the frst step n face detecton process n color ages. Many researchers use skn color odels to locate potental face regons and then exane the locatons of faces by analyzng each face canddate s shape and physcal geoetrc nforaton. In order to represent the huan face, fndng the effcent nvarant features for face detecton s stll an open proble. Real-te object trackng s a crtcal task n coputer vson applcatons. Many trackng algorths have been proposed to overcoe the dffcultes arsng fro nose, occluson, clutter and changes n the foreground object or n the background envronent. Aong the varous trackng algorths, ean shft trackng algorths have recently becoe popular due to ther splcty and effcency [10, 13, 16, 17, 18]. In the object trackng syste, face trackng s pervasve applcaton. In face trackng syste, face tracker estates the face trajectory by locatng ts poston n every frae of the sequence. Whle ths nforaton ay be suffcent for soe applcatons (e.g. detectng the presence of an ntruder), other applcatons requre addtonal data, lke knowng the orentaton, extenson or even the precse contour of the faces at every frae (e.g. facal expresson recognton). The ean shft algorth was orgnally proposed by Fukunaga and Hostetler[14] for data clusterng. It was later ntroduced nto the age processng county by Cheng[11]. Bradsk[15] odfed t and developed the Contnuously Adaptve Mean Shft (CAMSHIFT) algorth to track a ovng face. Coancu and Meer[1] successfully appled ean shft algorth to age segentaton[1] and Coancu et al,[13] have appled t to object trackng[13]. Mean Shft s an teratve kernel-based deternstc procedure whch converges to a local axu of the easureent functon wth certan assuptons on the kernel behavors. Furtherore, ean shft s a low coplexty algorth, whch provdes a general and relable soluton to object trackng and s ndependent of the target representaton. In the present paper, a novel algorth s proposed for the face regon segentaton n a vdeo sequence of ages usng fuzzy geoetrc face odel, whch eploys the ean shft to track the face along the vdeo streang. MATERIALS AND METHODS The Honda/UCSD Vdeo Database provdes a standard vdeo database for evaluatng face detecton, trackng and recognton algorths. Each vdeo sequence s recorded n an ndoor envronent at 15 fraes per second, and each 41

2 Internatonal Journal of Advanced Trends n Coputer Scence and Engneerng, Vol., No.1, Pages : (013) Specal Issue of ICACSE Held on 7-8 January, 013 n Lords Insttute of Engneerng and Technology, Hyderabad sequence lasted for at least 15 seconds. The resoluton of each vdeo sequence s 640x480. Every ndvdual s recorded n at least two vdeo sequences. In each vdeo, the person rotates and turns hs/her head n hs/her own preferred order and speed, and typcally n about 15 seconds, the ndvdual s able to provde a wde range of dfferent poses. The Honda/UCSD Vdeo Database contans two datasets. The frst dataset s recorded by a SONY EVI-D30 caera at Honda Research Insttute n 00. It ncludes three dfferent subsets, one each for tranng, testng, and occluson testng. Each subset contans 0, 4, 13 vdeos, respectvely, fro 0 (facal features) are brghter than the background. In order to ake the essental facal features clearly vsble, fltered age s converted nto bnary frae by sple global thresholdng. Further, the frae s denosed by orphologcal operatons, n whch openng operaton s perfored to reove nose, and then the closng operaton s perfored to reove holes. Then the actve pxels are grouped nto axal connected blocks to get the regons or blocks whch are labeled. After the labelng process, for each feature block, ts center of ass(x, y), orentaton, boundng rectangle and the length of se ajor axs are coputed. The resultant ages n dfferent steps of the face huan subjects. The second dataset s recorded by a SONY DFW-V500 caera at Coputer Vson Laboratory, Unversty of Calforna, San Dego, n 004. It ncludes two subsets, one each for tranng and testng, of 30 vdeos fro another 15 dfferent huan subjects [7]. The proposed algorth s experented wth the vdeo sequences drawn fro the above data sets. PROPOSED METHODOLOGY The proposed ethodology coprses the applcaton of fuzzy geoetrc face odel for face detecton and ean shft for face trackng n vdeo sequences, whch are descrbed below. A. Fuzzy Geoetrc Face Model for Detecton The fuzzy geoetrc face odel for face detecton [8] s appled to the nput vdeo sesquence. The extracted frae age s preprocessed and then the eyes are searched on the bass of geoetrcal knowledge of the syetrcal relatons between eyes. The other pronent feature, naely, outh, s searched wth respect to the detected eyes usng fuzzy rules and the face detecton algorth. The fuzzy rules are derved fro the knowledge of the relatve postons of the facal features n the huan faces and the trapezodal fuzzy ebershp functons to represent the uncertanty of the locatons of the facal features due to varatons n poses and facal expressons. The fraes of the nput vdeo sequence are expected to contan, not too dark or too brght, ages. If the nput frae of the vdeo sequence s a color frae, t s converted nto gray scale age. The gray scale frae s fltered usng the Sobel horzontal edge ephaszng flter and utlzng the soothng effect by approxatng a vertcal gradent. In the fltered frae, objects of nterest detecton algorth usng fuzzy geoetrc face odel are shown n the Fg. 1. B. Mean Shft Algorth There are varous ethods avalable for trackng an object n a vdeo sequence. They can be categorzed as: deternstc and probablstc. Deternstc ethod looks for the local axa of a slarty easure between the object odel and the target teratvely, and ean-shft s one such technque for trackng a ovng object. Mean shft s a versatle algorth that has found a lot of practcal applcatons - especally n the coputer vson where the densons are usually low. Hence, the ean shft s used to perfor lots of coon tasks n achne vson. The ean shft algorth s a non-paraetrc ethod. It provdes accurate localzaton and effcent atchng wthout expensve exhaustve search. It s an teratve process, whch coputes the ean shft value for the current pont poston, then oves the pont to ts ean shft value as the new poston, and copute the ean shft untl t fulflls certan condton [9]. Mean shft s a nonparaetrc densty gradent estator. It s eployed to derve the object canddate that s the ost slar to a gven odel whle predctng the next object locaton. In other words, t starts fro the poston of the odel n the current frae and then searches n the odel s neghborhood n next frae, followed by fndng best canddate by axzng a slarty functon. Fnally, the sae process s repeated n the next par of fraes. Mean shft consders feature space as an eprcal probablty densty functon. If the nput s a set of ponts, then Mean shft consders the as sapled fro the underlyng probablty densty functon. If dense regons are present n the feature space, then they correspond to the ode (or local axa) of the probablty densty functon. For each data pont, ean shft assocates t wth the nearby peak of the dataset's probablty densty functon and defnes a wndow 4

3 Internatonal Journal of Advanced Trends n Coputer Scence and Engneerng, Vol., No.1, Pages : (013) Specal Issue of ICACSE Held on 7-8 January, 013 n Lords Insttute of Engneerng and Technology, Hyderabad around t and coputes the ean of the data pont. Then t shfts the center of the wndow to the ean and repeats the algorth tll t converges. After each teraton, hence, the wndow shfts to a denser regon of the dataset. A target s usually defned by a rectangle or an ellpsodal regon n the age. Most exstng target trackng schees use the color hstogra to represent the rectangle or ellpsodal target. In ths paper, a new target representaton approach s adopted by usng the fuzzy geoetrc face odel and ean shft algorth. Frstly, the target representaton n the ean shft trackng algorth s descrbed n the followng: Denote by x * 1 n the noralzed pxel postons n the target regon, whch s supposed to be centered at the orgn pont. The target odel q correspondng to the target regon s coputed as q quu 1, n (1) * * qu C k( x ) ( b( x ) u) 1 where q u represent the probabltes of feature u n target odel q, s the nuber of feature spaces, s the * Kronecker delta functon, b( x ) assocates the pxel * x to the hstogra bn, k( x ) s an sotropc kernel profle and constant C s a noralzaton functon defned by n * C 1/ k( x ) () 1 Slarly, the target canddate odel p( y ) correspondng to the canddate regon s gven by p ( y) { p u ( y)} u 1 n h y x (3) p( y) Ch k ( b( x ) u) 1 h C (4) 1 h n h 1 k y x h where pu ( y ) represents the probablty of feature u n the canddate odel ( ) p y, 1 x n denote the pxel postons n the target canddate regon centered at y, h s the bandwdth and constant C h s a noralzaton functon. In order to calculate the lkelhood of the target odel and the canddate odel, a etrc based on the Bhattacharyya coeffcent s defned between the two noralzed hstogras p( y ) and q as follows: h 43 ( p ( y), q ) p ( ) u y q (5) u1 The dstance between p( y ) and q s then defned as u d( p ( y), q ) 1 ( p ( y), q ) (6) Mnzng the dstance gven by the Eq.(6) s equvalent to axzng the Bhattacharyya coeffcent gven by the Eq.(5). The teratve optzaton process s ntalzed wth the target locaton y 0 n the prevous frae. Usng Taylor expanson around p ( ) u y 0, the lnear approxaton of the Bhattacharyya coeffcent s obtaned as n 1 1 h y x ( p( y), q) pu ( y0) qu Ch w k u1 1 h (7) where q u w ( b( x ) u) u p ( y ) (8) 1 u 0 Snce the frst ter n Eq.(7) s ndependent of y, to nze the dstance n the Eq.(6) s to axze the second ter n the Eq.(7). In the teratve process, the estated target oves fro y to a new poston y 1, whch s defned as n h y x x 1 w g h y1 (9) n h y x w 1 g h When the kernel g wth the Epanechnkov profle desgned by the Eq.(9) s reduced to y 1 (10) nh x 1 w nh w 1 By usng Eq.(10), the ean shft trackng algorth fnds, n the new frae, the ost slar regon to the object. The ean shft algorth can be extended to be used wth sequence of colour ages and to ft wth the varyng scale of the object throughout the sequence. It works well n the absence of occluson. C. Proposed Method The bass for the proposed algorth for face detecton and trackng n a vdeo sequence s the object-trackng algorth for a ovng target. A rectangular target wndow s defned n an ntal frae of detected face regon by applyng fuzzy geoetrc face odel, and then the data wthn that wndow s processed to track the object n the vdeo sequence usng

4 Internatonal Journal of Advanced Trends n Coputer Scence and Engneerng, Vol., No.1, Pages : (013) Specal Issue of ICACSE Held on 7-8 January, 013 n Lords Insttute of Engneerng and Technology, Hyderabad ean shft algorth. The block dagra of the proposed ethod for face detecton and trackng n a vdeo sequence s shown n the Fg.. The algorth of the proposed ethod s gven below: Algorth: Face detecton and trackng (1) Input vdeo sequence. () Extract all the fraes fro the nput vdeo sequence, and then select frst vdeo frae as key frae. (3) Apply the fuzzy geoetrc face odel[9] for searchng the face regon n the key frae usng pronent face features, naely, eyes and outh, to detect the face. (4) Select the next vdeo frae and perfor the ean shft coputaton to deterne the object oton fro one vdeo frae to the next frae. (5) Draw the rectangular box for the detected face n the frae. (6) Repeat the Step 4 and 5 tll the end of the nput vdeo sequence, whch results n trackng the detected face n the vdeo sequence. geoetrc face odel. Then, the consecutve fraes fro a vdeo sequence and ther correspondng ean shft are estated and face s tracked. In the proposed ethod, sngle frontal face n the vdeo fraes wth dfferent otons, head tlts, lghtng condtons, expressons and backgrounds are consdered. Table 1: Coparson of the trackng results obtaned by the proposed ethod and other ethods Paraeters Jfeng Nng Shaohua Zhou et Proposed Method et.al[9] al. [19] Vdeo Face Face Face #vdeo fraes Frae rate 10 fps fps Frae Sze 35x40 40x x480 Occluson No Yes Yes The proposed approach yelds better average detecton and trackng, whch s robust and alost real te. The pleentaton of the proposed ethod s evaluated wth nuerous experents, whch yelded encouragng results that deonstrate the effectveness n detectng and trackng faces n vdeos. The proposed ethod can be extended for EXPERIMENTAL RESULTS AND DISCUSSION The extensve and representatve experents are perfored to llustrate and testfy the proposed fuzzy geoetrc face detecton and ean shft trackng based odel. The experentaton of the proposed approach s carred out usng the Honda/UCSD Vdeo database [7]. The pleentaton s done on Intel CoreQuad GHz achne usng MATLAB 7.0. The dfferent fraes of the nput vdeo sequence are extracted. The extracted frae age s preprocessed and then the facal features, naely, eyes and outh are searched by usng fuzzy geoetrc face odel [8], and then, the detected face s tracked by usng ean shft algorth [9]. The experental results of the face detecton and trackng n vdeo sequence by the proposed ethod are shown n the Fg. 3. The coparson of the trackng results obtaned by the proposed ethod and other ethods n the lterature are shown n the Table 1. CONCLUSION In ths paper, a novel ethod for detecton and trackng of a face n a vdeo sequence based on the fuzzy geoetrcal face odel and ean shft trackng s presented. The huan face s detected by feature extracton process based on fuzzy 44 ultple faces n a vdeo sequence by consderng ultple face detecton and trackng algorths. ACKNOWLEDGEMENT The authors are ndebted to the Unversty Grants Cosson, New Delh, for the fnancal support for ths research work under UGC-MRP F.No.39-14/010 (SR). REFERENCES [1] A. Ylaz, O. Javed and M. Shah, Object trackng: A survey, ACM Coput. Surv. 38(4) (006). [] Knjal A Josh, Darshak G, Thakore, A survey on ovng object detecton and trackng n vdeo survellance syste, Internatonal Journal of Soft Coputng and Engneerng Vol., No. 3, 01, pp [3] Huafeng Wang, Yunhong Wang andyuancao, Vdeo based face recognton: A Survey, World Acadey of scence, Engneerng and Technology, pp [4] Andrea F. Abate, Mchele Napp, Danel Rcco, Gabrele Sabatno, D and 3D face recognton: A survey, Pattern Recognton Letters 8, 007, pp [5] Ing-Sheen Hseh, Kuo-Chn Fan, and Chunhsun Ln, A Statstc Approach to the Detecton of Huan Faces n Color Nature Scene, Pattern Recognton, 35, 00, pp [6] J. G. Wang and T. N. Tan, A New Face Detecton Method Based on Shape Inforaton, IEEE Transactons on Pattern Recognton Letters, Vol. 1, 000, pp [7] Kuang-Chh Lee, Jeffrey Ho, Mng-Hsuan Yang, Davd Kregan, Vsual trackng and recognton usng probablstc appearance

5 Internatonal Journal of Advanced Trends n Coputer Scence and Engneerng, Vol., No.1, Pages : (013) Specal Issue of ICACSE Held on 7-8 January, 013 n Lords Insttute of Engneerng and Technology, Hyderabad anfolds, Coputer Vson and Iage Understandng, Vol. 99, 005, pp [8] P. S. Hreath and Manjunath Hreath, Fuzzy face odel for face detecton usng eyes and outh features, Internatonal Journal of Machne Intellgence, Vol. 3, No. 4, 011, pp [9] Jfeng Nng, Le Zhang, Davd Zhang and Chengke Wu, Robust Object Trackng usng Jont Color-texture Hstogra, Internatonal Journal of Pattern Recognton and Artfcal Intellgence Vol. 3, No. 7 (009), pp [10] G. Bradsk, Coputer vson face trackng for use n a perceptual user nterface, Intel Technologes. Journal. Q() (1998), pp.1 1. [11] Yzong Cheng Y. Cheng, Mean Shft, Mode Seekng, and Clusterng, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 17, NO. 8 (1995), pp [1] D. Coancu and P. Meer, Mean shft: a robust approach toward feature space analyss, IEEE Transactons Pattern Analyss and Machne Intellgence. Vol.4(5) (00), pp [13] D. Coancu, V. Raesh and P. Meer, Kernel-based object trackng, IEEE Transactons Pattern Analyss and Machne Intellgence. Vol. 5(5) (003), pp [14] K. Fukunaga and L. D. Hostetler, The estaton of the gradent of a densty functon, wth applcatons n pattern recognton, IEEE Trans. Infor. Th.1(1) (1975), pp [15] G. Bradsk, Coputer vson face trackng for use n a perceptual user nterface, Intel Technol. J.() (1998), pp.1 1. [16] I. Hartaoglu and M. Flckner, Detecton and trackng of shoppng groups n stores, Proc. IEEE Conf. Coputer Vson and Pattern Recognton, Kaua, Hawa, 001, pp [17] Q. A. Nguyen, A. Robles-Kelly and C. Shen, Enhanced kernel-based trackng for onochroatc and therographc vdeo, Proc. IEEE Conf. Vdeo and Sgnal Based Survellance(006), pp [18] C. Yang, D. Raan and L. Davs, Effcent ean-shft trackng va a new slarty easure, Proc. IEEE Conf. Coputer Vson and Pattern RecogntonI(005), pp [19] Shaohua Zhou, Raa Chellappa, Baback Moghadda, Vsual Trackng and Recognton Usng Appearance-Adaptve Models n Partcle Flters, IEEE Transactons on Iage Processng, Vol. 13, No. 11, 004, pp

6 Internatonal Journal of Advanced Trends n Coputer Scence and Engneerng, Vol., No.1, Pages : (013) Specal Issue of ICACSE Held on 7-8 January, 013 n Lords Insttute of Engneerng and Technology, Hyderabad Fg 3: The experental results obtaned by the proposed ethod of the face detecton and trackng n a saple vdeo sequence 46