Cast Indexing for Videos by NCuts and Page Ranking

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1 Cast Indexng or Vdeos by NCuts and Page Rankng Yong Gao, Tao Wang, Janguo L, YangZhou Du, We Hu, Ymn Zhang, * HaZhou A Intel Chna Research Center, Beng, P.R. Chna * Det. CS. &Tech. o Tsnghua Unversty, Beng, P.R. Chna { yong.y.gao, tao.wang, anguo.l, yangzhou.du we.hu, ymn.zhang }@ntel.com ahz@mal.tsnghua.edu.cn ABSTRACT Cast ndexng s an mortant vdeo mnng technque whch rovdes audence the caablty to ecently retreve nterested scenes, events, and stores rom a long vdeo. Ths aer rooses a novel cast ndexng aroach based on Normalzed Grah Cuts NCuts) and Page Rankng. The system rst adots ace tracker to grou ace mages n each shot nto ace sets, and then extract local SIFT eature as the eature reresentaton. There are two key roblems or cast ndexng. One s to nd an otmal artton to cluster ace sets nto man cast. The other s how to exlot the latent relatonshs among characters to rovde a more accurate cast rankng. For the rst roblem, we model each ace set as a grah node, and adot Normalzed Grah Cuts NCuts) to realze an otmal grah artton. A novel local neghborhood dstance s roosed to measure the dstance between ace sets or NCuts, whch s robust to outlers. For the second roblem, we buld a relaton grah or characters by ther co-occurrence normaton, and then adot the PageRank algorthm to estmate the Imortant Factor IF) o each character. The PageRank IF s used wth the content based retreval score or nal rankng. Extensve exerments are carred out on moves, TV seres and home vdeos. Promsng results demonstrate the eectveness o roosed methods. Categores and Subect Descrtors H.3.1 [Inormaton Storage and Retreval]: Content Analyss and Indexng. General Terms Algorthms, Management, Desgn, Exermentaton. Keywords Cast Indexng, Man Cast Detecton, Cast Rankng, Local Neghbor Dstance, NCuts, Page Rankng. 1. INTRODUCTION The ongong exanson o vdeo data n World Wde Web, move, Permsson to make dgtal or hard coes o all or art o ths work or ersonal or classroom use s granted wthout ee rovded that coes are not made or dstrbuted or rot or commercal advantage and that coes bear ths notce and the ull ctaton on the rst age. To coy otherwse, to reublsh, to ost on servers or to redstrbute to lsts, requres ror secc ermsson and/or a ee. CIVR'07, July 9-11, 2007, Amsterdam, The Netherlands. Coyrght 2007 ACM /07/ $5.00. TV rograms, and home vdeos has generated ncreasng requrements or semantc based vdeo mnng technques, such as news abstracton, sorts hghlghts detecton, move/tv/home vdeos ndexng and retreval etc. As the man subect o scenes, events and stores n these vdeos, characters are one o the most mortant contents to be ndexed. By cast ndexng, we can dscover actve characters who requently aear n the vdeo and retreve ther assocated vdeo cls or ecent browsng. For cast ndexng, human ace s one o the most mortant vsual cues besdes auxlary cues o seech, and clothes etc. Automatc ace detecton and recognton technques can be emloyed as man ways and means or cast ndexng. However aces n vdeos, esecally lms, stcoms, and home vdeos, usually have large varatons o ose, exresson and llumnaton whch ust are the reasons why relable ace recognton s stll a very challengng roblem n ractce[11]. To reduce the adverse eect o those varatons n mage/vdeo based ace recognton, a lot o methods have been roosed. Arandelovc and Zssserman roose to aly ane warng and llumnaton correctng on ace mages to allevate the adverse eects nduced by ose and llumnaton varatons [2]. But t s unable to deal wth the out-o-lan ace rotaton. In [1] and [7], ace recognton s based on manold analyss. However the real manolds o aces and relatonshs among them n vdeos are too comlex to be accurately characterzed by smled models. Although 3D ace model s utlzed to enhance the vdeo-based ace recognton erormance [3], t s very dcult to accurately recover the head ose arameters by the state-o-the-art regstraton technques and not ractcal or real-world alcaton. In a word, t s very hard to buld a robust cast ndexng system only based on ace recognton technques. Fortunately, n eature-length lms, e.g., move and TV seres, the man characters requently aear n derent shots, resultng n large numbers o contnuous aces. The rch dynamc acal normaton rovdes mult-vew ace exemlars o the same erson, whch make t ossble to detect man characters by clusterng aces. Accordng to the dea, ths aer rooses a novel case ndexng aroach. Frst, near rontal aces are detected and tracked nto ace sets by ace tracker and local SIFT eatures are extracted on normalzed aces. Then we use ace set as the basc rocessng unt to detect man characters that s essentally a attern clusterng roblem. To reduce adverse eects o ace outlers, a local neghbor dstance s roosed to measure the smlarty between ace sets. By modelng ace sets as nodes o a grah, Normalzed Grah CutsNCuts) s emloyed to nd the otmal artton. The NCuts based clusterng results rovde a

2 Shot and Scene detecton Face trackng Landmark onts detecton Local SIFT eature extracton Feature extracton module Dstance between ace sets Man cast detecton by NCuts Man cast rankng by IF Cast ndexng module Fgure 1. Framework o the roosed cast ndexng system content-based browsng result, but not rank how mortant each character s. Motvated by the successul age rankng technology o Google web search [6], we roose a novel character mortant actor IF) aroach to rank cast lst, whch s a used score o Page Rankng, aearng tme and requency. By age rankng, we can also dscover the latent relatonshs between characters. The aer s organzed as ollows. In Secton 2, the cast ndexng system and eature extracton module are rst ntroduced. Then we roose an mroved dstance measure and the NCuts based man cast detecton aroach. To rank characters by ther mortance actor, a cast rankng method s roosed. Fnally, extensve exermental results are reorted n Secton 3, ollowed by the conclusons n Secton SYSTEM FRAMEWORK The cast ndexng system conssts o three man modules as llustrated n Fgure 1. In the eature-extracton module, shot boundary and scene segmentaton are rst detected. Then near rontal aces are detected and tracked by ace tracker n each shot. At last, local SIFT eatures are extracted on normalzed ace mages. In the man cast detecton module, man characters are detected by usng NCuts algorthm to cluster ace set. Fnally, or ecent browsng, cast rankng module sorts characters by ther mortance actor IF). 2.1 FEATURE EXTRACTION MODULE Smlar to text rocessng based on word, sentence, aragrah and document, vdeo mnng can be analyzed rom our levels,.e., rame, shot, scene and the whole vdeo sequence level. A shot s a set o vdeo rames catured by a sngle camera n one consecutve recordng acton. A scene s dened as one o the subdvsons o a vdeo n whch the settng s xed and tme contnuous, or resentng the contnuous acton n one lace. Our system uses a shot detecton algorthm [4] to detect shots. Based on detected shots, we cluster temoral-satal coherent shots nto scenes by NCuts algorthm and color hstogram eatures[10]. In each shot, we use ace tracker [14] to detect and track near rontal aces and outut ace sets. As the basc rocessng unt o cast ndexng, ace sets not only rovde ror knowledge about mult-vew acal exemlars whch belong to the same erson but also decrease the data sze o ace clusterng algorthm n man cast detecton. For each ace, actve shae model ASM) based ace algnment algorthm [8] s utlzed to detect 88 acal landmarks as shown n g. 2b). Usng these landmarks, ace mages are geometrcally normalzed nto a standard orm by ane transormaton to remove the varatons o translaton, scale, n-lane and slght out-o-lane rotaton. It s demonstrated that local eatures outerorm global ones n most recognton and vercaton tasks because they are more robust to artal occlusons, ose and llumnaton varatons. We extract the SIFT eatures [9] on ve local regons whch coverng two eyes, central regons o two eyes, nose and orehead, as shown n Fgure 2c). 2.2 DISTANCE MEASURE The ace tracker grous same character s aces n each shot nto ace sets. However, characters may aears n multle shots, and hence n multle ace sets. As llustrated n Fgure 3, the am o man cast detecton s to cluster ace sets nto bgger aggregaton whch ncludes all the ace sets o the same character. For ace set clusterng, dstance measure between two ace sets s a key ssue. a) b) c) Fgure 2. Local SIFT eature extracton. a) The orgnal ace mage; b) Detected 88 landmarks by ace algnment; c) Fve local regons or SIFT eature extracton. Fgure 3. Man cast detecton by clusterng ace sets

3 The ollowng asects should be consdered: 1) The number o ace mages n ace sets s derent, whch are relevant to the ace aearng tme n derent shot. I the ose, exresson and llumnaton o aces dynamcally changes n a ace set, they can rovde rch mult-vew exemlars o the same erson, whch s very useul to brdge ace sets wth overlaed ace exemlars or ace clusterng. On the contrary, aeared aces n a ace set are statc or the ace number s very small, t wll be not very normatve. 2) The manolds o ace sets n eature sace are very comlex and qute derent rom each other. Fgure 4 shows manolds o our ace sets o two characters n the oular Korea TV seres DaChangJn. It can be observed that aces o the same erson can be very dstant, whle derent erson s aces may be very near n the eature sace. However, two ace sets wth bgger overla generally belong to the same erson wth hgher robablty. 3) Unavodably, there are outlers due to msalgnment and varatons o ose, exresson or llumnaton etc. When clusterng ace sets, t wll roagate the error and merge ace sets o derent ersons nto the same cluster. Fortunately, there s rch acal no n eather length lms. Snce outlers don t occurred requently, aces wth hgher densty dstrbuton n the eature sace relably belong to same erson. We have one undament n mnd: normal samles have suort rom ther nearest neghborhood same-ace-set Face-set 1 Face-set 2 Face-set 3 Face-set 4 a) samles, whle outlers do not Dstance Measure between Two Faces Based on the above observaton 3), we dene dstance measure between two ace mages by consderng ther nearest neghborhood suort normaton. In ths aer, k-nearest neghbor s adoted. Let S and S are two ace sets, or two aces xm S and x n S, the local neghbor dstance between x m and x n s dened as: 1 d xm, x n) = x x 2 q 1) k x Ν xm ), x q Ν x n ) where Ν x m ) s the k neghbors o x m n S and Ν x n ) s the k neghbors o x n n S,. denotes L 2 dstance between two aces. It can be easly roved: d x, x ) x x 2) m n q x Ν xm ) x q Ν x n ) It s to say that the dstance measure dened by Eq1) s equvalent to rst aly smooth lter on manold to weaken or remove outler dsturbances, then calculated dstance between two averaged data onts. As llustrated n g. 5, the local neghbor dstance uncton o Eq1) s more robust to outler than smle L2 dstance x x. m n Dstance Measure between Two Face Sets As mentoned n the observaton 2), the ace sets wth bgger overla generally belong to the same erson wth hgher robablty. For the dstance measure between two ace sets, t s ntutve to summarze l mnmum local neghbor dstances d x m, x ) to evaluate the ace set overla. n d S, S ) = l l x S, x S m mn n d x m, x Where mn l s the l th mnmum dstance o d x m, x ), n )) n 3) dr 1,g 1 ) ace set G g 1 ace set R r 1 dr 1,b 1 ) ace set B b 1 b) Fgure 4. Comlexty o the ace set manolds n eaturelength lms. a) Reresentatve ace mages o our ace sets o two characters. b) Corresondng manolds are vsualzed by rst two dmensons o PCA subsace. Fgure 5. Dstance measure between two aces that comes rom two ace sets resectvely. The cross onts are the averaged onts o local neghbor onts o r 1, g 1, and b 1 resectvely. Although green onts g 1 s more near to red ont than blue onts b 1, the local neghbor dstance dr 1, g 1 )>dr 1, b 1 ) snce g 1 onts has lower local densty than b 1 onts.

4 xm S and x n S. I there s an enough overla between ace set S and S, or the two ace sets are near enough searately, the summary o mn l d xm, x n )) wll be small. And ace set S and S belong to the same erson wth hgh robablty. Otherwse, we won t make decson to merge the two ace sets untl they are brdged by other neghbored ace sets. In summary, emloyng local neghbor dstance elmnates the outler dsturbance and acheves more robust erormance to measure the smlarty between ace sets as llustrated n g MAIN CAST DETECTION Ater the dstance measure between ace sets s dened, the man cast detecton s modeled as a grah arttonng roblem,.e. grah cut. All ace sets are reresented as a weghted undrected grah G = V, E), where the nodes V o the grah are the ace sets and the edges are the smlartes between ar-wse ace sets. For ace set clusterng, we seek the best artton C, C, 1 2 K, C to m satsy that the smlarty among the nodes n a sub-grah C s hgh and across smlarty between sub-grahs C, C ) s low. To otmally artton a grah consttuted by ace sets, the normalzed cut algorthm NCuts) s emloyed Normalzed Grah Cuts The normalzed cut s rst roosed by Sh and Malk [5]. Here we gve a bre ntroducton. A grah G = V, E) can be arttoned nto two dsont sub-grahs A and B wth A B = V and A B = Φ, by smly removng edges connectng the two arts. The degree o dssmlarty between these two sub-grahs can be comuted as total weght o the edges that have been removed. In grah theoretc language, t s called the cut: cut A, = w u, v) 4) u A, v B The otmal bartton o a grah s the one that mnmze ths cut value. To avod unnatural bas or arttonng o small sets o onts, Sh and Malk roosed the dsassocaton measure o the normalzed cut Ncut) [5]: cut A, cut A, Ncut A, + assoc A, V ) assoc B, V ) = 5) where assoc A, V ) = u A t w u, t) s the total connecton rom, V nodes n A to all nodes n the grah and assoc B, V ) s smlarly dened. Gven a artton o the grah,.e., dvde V nto two dsont sets A and B, let X be a N = V dmensonal ndcaton vector, x = 1 node s n A and -1 node s n B. Let d ) = w, ) and D be and N N dagonal matrx wth d on ts dagonal, W be an N N symmetrcal matrx wth W, ) = w, the aroxmate dscrete soluton to mnmzng NCuts can be ound ecently by solvng the generalzed egenvalue system, D W ) Y = λdy 6) where Y s a lnear transormaton o X and can be used or artton by a threshold The Man Cast Detecton Algorthm For two ace sets S and S, the grah edge weght w s dened as: 2 d S, S ) 2 σ w = e S s the n nearest neghbor o S 7) 0 otherwse In our exerments, n s set to the square root o the number o ace sets N and σ s set to 0.8 whch s about the threshold that two aces are rom the same character n SIFT eature sace. Based on NCuts clusterng aroach, the man cast detecton algorthm conssts o the ollowng stes: 1. Gven ace sets detected n the eature extracton module, set u a weghted grah G = V, E) usng dstance uncton dened by Equaton 2) and 4). 2. From the grah, create matrx W and D. And solve egenvalue system D W ) x = λdx. 3. Use the egenvector wth the second smallest egenvalue to bartton the grah by ndng the slttng ont wth mnmum Ncut. 4. Recursvely aartton the sub-grah when stong crteron s not satsed. Whether contnue to bartton a sub-grah s decded by tryng a new bartton. A sub-grah should be arttoned ether o the ollowng two condtons are satsed: 1. The NcutA, o the tryng bartton s below a reselected value. 2. Comutng the hstogram o the egenvector values and the rato between the mnmum and the maxmum values n the bns s not smaller than a re-selected threshold. 2.4 MAIN CAST RANKING In Secton 2.3, the man cast detecton module oututs ace set clusters o requently aeared characters. To sort the mortant characters and analyze ther relatonshs n scenes, we urther rank man casts clusters o ace sets) by Imortance Factor IF). It s natural that mortant actors generally aear wth hgh aearng tme or aearng requency. Furthermore, as the hnge o story scenaro, mortant actors requently aear wth others n scenes, e.g. dalog, ghtng etc. Thus the character mortance and ther close relatonshs can be dscovered rom ther ont aearng requency n scenes. Motvated by the successul PageRank technology o Google web search [6], each character can be vewed as a web age and ontly aeared multle characters n a scene can be vewed as lnked edges among them. It s ntutve that one character has many connected edges wth others, or t s connected to some mortant ersons, the PageRank value s bg. In our system, we rank the detected characters by Imortance Factor IF), whch uses scores o PageRank [12], aearng tme and aearng requency by a lnear weghted average. For a character C, hs/her IF can be calculated by the ollowng ormula

5 IF C ) wt At C ) + w A C ) + w A C ) = 8) where At ) s the Aearng Tme AT) score, A ) s the Aearng Frequency AF) score and A ) s the PageRank score. In our exerments, w t = 0. 2, w = 0. 3, w = 0. 5 are ther corresondng weghts. The PageRank score s calculated usng the algorthm roosed by Lawrence Page and Sergey Brn [12]. For characters C, =1,2..N, the PageRank value o C s dened as the ollows: A C ) = 1 d) + d A C ) / L C ), =1,..,N 9) where A C ) s the PageRank score o the character C, A C ) s the PageRank score o character C whch lnks to character C,.e. C ontly aear wth C n a same scene. L C ) s the outbound lnks o C,.e., the number o characters who ontly aeared wth C. d s a damng actor n [0, 1]. Each character s assgned a startng PageRank value A C ) =1 and the damng actor s set to 0.8. Then an teratve scheme s emloyed to calculate the PageRank scores o each character. Lawrence Page and Sergey Brn have roved that the PageRank value wll converge to the real rank values no matter what the ntal rank values A C ) are. The aearng tme At C ) and the aearng requency A C ) scores are calculated accordng to the character aearng tme and the clustered ace set number. In detal, the two scores are Shots & Scenes Face sets Scene1 Characters & Relatonshs Cast rankng Scene2 Cr Scene3 C r > C b > C g Scene4 Scene5 Fgure 6 An llustratve examle o three characters C r, C g, and C b aeared n a vdeo wth 6 scenes. The undrected edge reresents two ace sets ontly aeared n a scene. The ellse sze reresents the aearng tme. Table 1 Imortance Factors o each character n g. 5. It can be observed that Cr has the hghest IF score and s detected as the most mortant character. Character Cr Cg Cb AtC) AC) LC) AC) IFC) Cg Cb Scene6 dened as ollows. A C ) A t C ) = C ' s aearng tme C ' s aearng tme = 10) C ' s ace set number 11) C ' s ace set number The aearng tme and aearng requency scores are useul actors when we udge whether a character s mortant by hmsel. And the PageRank scores relect the mortance o character relatonshs. The character that aears wth more other characters and more mortant characters wll get hgher PageRank score. By ths way, the character relatonshs can be drectly got by the PageRank analyss. An llustratve examle s shown n Fgure 6. The calculated IF scores are lsted n table 1. It can be observed that C r s more mortant than Cg Snce Cr has more relatonshs wth others than Cg. Although Cb aears wth the longest tme AtCb)=0.6, t s not mortant due to bad aearng requency score ACb)=0.2 and age rank score ACb) = EXPERIMENTS To evaluate erormance o the roosed cast ndexng aroach, we conducted exerments on our vdeos whch are one Hollywood move 007 De Another Day 007), two TV seres DaChangJn DCJ), rends FR), and one home vdeoshv). For each vdeo, two ersons are manually annotated wth ground truth. The detals are lsted n Table EVALUATION METRIC ~ The normalzed mutual normaton I X, Y ) [12] s utlzed to evaluate the man cast detecton erormance. Based on normaton theory, mutual normaton s a deendence measure or two random varables whch s dened as: I x, y) X, Y ) = x, y)log 12) x X y Y x) y) The normalzed mutual normaton s calculated by: ~ I X, Y ) I X, Y ) = 13) max H X ), H Y )) where H X ) and H Y ) are the entroy o the cluster sets X and Y resectvely. It s obvous that I ~ s n the range o [0,1]. The more smlar the dstrbuton between X and Y s, the bgger ~ I X, Y ) s. Thus the bgger value I ~ X, Y ) means the better clusterng erormance. Table 2 Statstc o our testng vdeos Vdeo name 007 DCJ FR HV Vdeo length 4022s 2680s 1365s 2885s Shots number Scenes number Face Set Characters

6 3.2 MAIN CAST DETECTION RESULTS Fgure 7 llustrates the detected man cast lst and manually annotated ones o the our test vdeos, where the mssng man casts, non-man-casts and dulcate casts are labeled wth M, N and D resectvely. From the gure, t can be observed that the cast ndexng system can dscover the man cast o the our vdeos well totally 39 man characters, mssng 5) wth very low alse detecton rate only 2 non-man-casts are detected). One man roblem s the dulcate characters due to bg varatons o ace ose, exresson and llumnaton etc. We beleve that the dulcate case can be reduced by better ace algnment rerocessng technques. We also quanttatvely evaluate the erormance o the man cast detecton usng mutual normaton by comarng automatcally clusterng results wth manually labeled ace sets. For comarson, several other methods are also tested. Method 1: The roosed method n ths aer. Method 2: Usng NCuts to cluster ace sets and mnmum ace ar dstance between two ace sets, whch s dened as d S, S ) = mn x x 14) xm S, x n S where. denotes L 2 dstance between two aces n eature sace. Method 3: Usng HAC Herarchcal Agglomeratve Clusterng) to cluster ace sets and mnmum ace ar dstance between two ace sets Equaton 14) to measure the smlarty between them. Ths s our revous method [13]. m n Fgure 8. Mutual Inormaton o the our methods on the our test vdeos Fgure 7. Detected man cast o our vdeos. The cast lsts wth lght-green background are manually annotated ground truth. Blue box wth character M ndcates the mssng detected casts. Green box wth character D ndcates dulcate casts. Red box wth character N ndcates casts whch are not the man casts. Fgure 9. Ranked cast lst accordng derent methods. a) Manually ranked cast lst. b) Cast lst ranked by the roosed method. c) Cast lst ranked by aearng tme. d) Cast lst ranked by aearng requency.

7 Method 4: Usng HAC to cluster ace sets and local neghbor dstance roosed n ths aer to measure the smlarty between two ace sets. The mutual normaton values o these methods on derent vdeos are shown n Fgure 8. It s obvous that the roosed one NCuts+Local neghbor dstance) has the best erormance among the our clusterng methods. And the roosed local neghbor dstance can actually mrove the clusterng erormance or both NCuts and HAC. Excet on the vdeo 007 De Another Day, NCuts acheves much better erormance than HAC. One man roblem o HAC s that t roduces many solated data onts aggregaton o ace sets) or lower threshold whle t mxes data onts o derent classes or hgher threshold. Another observaton s that the erormance, measured by mutual normaton, vares greatly on derent vdeos. It acheves the best erormance on oular TV seres DaChangJn and Frends wth MI and the worst on home vdeo XAn wth MI Ths can be exlaned rom the asects o qualty o ace cture, shootng style and content o the story, whch wll greatly aect the cture qualty, ose and llumnaton varaton o ace mages n the vdeos. 3.3 MAIN CAST RANKING RESULTS Ater the man cast detecton, every man cast s IF s calculated accordng Equaton 8-11 and the cast lst s ranked accordng to the IF scores. Fgure 9 shows the ranked cast lsts o move 007 De Another Day sortng by derent schemes. In Fgure 9, a) s the manually ranked cast lst based the mortance o derent casts. Sub-Fgure b) s the ranked cast lst based on IF scores roosed n the aer. c) and d) are casts lsts ranked based on aearng tme and aearng requency. I we examne the lsts careully, t can be ound that the order o cast lst n Fgure 9 b) s more close to that o cast lst n Fgure 9 a). That s, the IF scores can relect the mortance o man casts more reasonably than aearng tme or aearng requency. 4. CONCLUSION Based on NCuts and age rankng, ths aer rooses a novel cast ndexng aroach or eature length lms. Gven an nut vdeo, ace tracker rst tracks aces rom each shot, and oututs ace sets. Then by clusterng smlar/overlaed ace sets, NCuts s emloyed to detect man characters. To deal wth outler aces rom varatons o ose, exresson, llumnaton and bad ace normalzaton, we use local neghbor dstance to measure the smlarty between ace sets. To nd the best artton on ace sets, normalzed cut s emloyed as a crteron to artton an undrected grah n whch every ace set s reresented by a node and the otmal soluton can be easly sought by solvng egenvalue roblem. Furthermore, ater man character detecton, a cast rankng emrcal ormula s roosed to rank characters and mnng ther relatonshs. Extensve exerments demonstrate that the NCuts and age rankng based cast ndexng aroach s a rutul drecton to exlore robust cast ndexng system. 5. REFERENCES [1] Arandelovc, O., Shakhnarovch, G., Fsher, J., Colla, R. and Darrell, T. Face Recognton wth Image Sets Usng Manold Densty Dvergence. In Proceedngs o the 2005 IEEE Comuter Socety Con. on CVPR. 2005, [2] Arandelovc, O. and Zsserman, A. Automatc Face Recognton or Flm Character Retreval n Feature-Length Flms. In Proceedngs o the 2005 IEEE Comuter Socety Con. on CVPR. 2005, [3] Everngham, M. and Zsserman, A. Identyng Indvduals n Vdeo by Combnng Generatve and Dscrmnatve Head Models. In Proceedngs o the Tenth IEEE Int. Con. On Comuter Vson. 2005, [4] Yuan J.H., Zheng W.J., Chen L., et. al. Tsnghua Unversty a TRECVID 2004: shot boundary detecton and hgh-level eature extracton, In NIST worksho o TRECVID [5] Janbo, S. and Malk, J. Normalzed Cuts and Image Segmentaton. IEEE Trans. On PAMI, vol 22, no. 8, 2000, [6] Lawrence, P. and Sergey, B. The PageRank Ctaton Rankng: Brngng Order to the Web. Stanord Dgtal Lbrary Technologes Proect, 1998 [7] Lee, K. C. and Kregman, D. Onlne Learnng o Probablstc Aearng Manolds or Vdeo-based Recognton and Trackng. In Proceedngs o the 2005 IEEE Comuter Socety Con. on CVPR. 2005, [8] Zhang L., A HZ, Lao SH, Robust Face Algnment Based On Herarchcal Classer Network, T.S. Huang et al. Eds.): HCI/ECCV 2006, LNCS 3979,.1-11, Srnger- Verlag Berln Hedelberg [9] Lowe, D. Dstnctve Image Features From Scale-Invarant Keyonts. Internatonal Journal o Comuter Vson. 2004, [10] Rasheed, Z., Shah, M., Detecton and reresentaton o scenes n vdeos. IEEE Transactons on Multmeda, Volume 7, Issue 6, Dec. 2005, [11] Stan Z., L., Jan, A. K. Handbook o Face Recognton. Srnger, NY, 2004 [12] Studholme, C., Hll, D. and Hawkes, D. An overla nvarant entroy measure o 3D medcal mage algnment. Pattern recognton. 1999, [13] Fan W., Wang T., Bouguet, J. Y., Hu W., et. al. Semsuervsed Cast Indexng or Feature-Length Flms. In Proeedngs o the 13 th Internatonal MultMeda Modellng Conerence MMM 2007). [14] L Y., A HZ, Huang C., Lao SH, Robust Head Trackng wth Partcles Based on Multle Cues Fuson, T.S. Huang et al. Eds.): HCI/ECCV 2006, LNCS 3979,.29-39, Srnger-Verlag Berln Hedelberg 2006.