Digital Interactive Kanban Advertisement System Using Face Recognition Methodology

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1 Coputatioal Water, Eergy, ad Eviroetal Egieerig, 2013, 2, doi: /cweee b005 Published Olie July 2013 ( Digital Iteractive Kaba Advertiseet Syste Usig Face Recogitio Methodology Feg-Yi Cheg, Chu-Ja Chag, Gwo-Jia Jog Departet of Electroics Egieerig, Natioal Kaohsiug Uiversity of Applied Scieces, Kaohsiug. Eail: Received May, 2013 ABSTRACT Most of advertiseet systes are presetly still lauch the publicity cotet by the static words ad pictures. Recetly, this static advertiseet odel will ot be able to attract people s attetio ore ad ore. Moreover, the static iforatio cotet of advertiseet syste is liited because of the layout show size. It ca ot also fully deostrate the iforatio cotet of advertiseet syste. I this paper, we develop a digital iteractive kaba advertiseet syste usig face recogitio ethodology to solve these probles. The syste captures the perso s face through the caera. The digital advertiseet cotet size is relevat by the perso ad caera observatio locatios. I this paper, we adopt the Adaboost algorith to judge people face, ad the syste oly eed to grab the positio of the face. The syste does t built expesive ad coplex equipet to reduce the syste cost ad ehace the syste perforace. This syste ca also achieve the sae siilar digital iteractive advertisig effectiveess. Keywords: Face Recogitio; Kaba Advertiseet; Adaboost; Iteractive 1. Itroductio There are various kids of advertisig edia ow. For exaple: televisios, broadcastigs, agazies, ewspapers, outdoor advertisig ad trasit advertisig etc. Beside above edia, the appearace of the web advertisig akes advertisig havig ore ad ore developet space. A advatage of web advertisig is that cosuers ca select advertisig freely ad obtai essages advertisig trasit iediately. Advertisig ca iteract with cosuers ad trasit feedback istatly, but traditioal advertisig ca t [1,2]. I this paper, iteractive ultiedia as the thee to explore the visual iterface desig ad iteractive ultiedia developet, accordig to the popular tred of today s iteractive ultiedia authorig the coo iteractive ultiedia such as: iteractive web pages, DVD ovies eu, web advertisig, teachig CD-ROM etc., because of the rapid growth of techology ad the techology atures, the traditioal static advertisig, web, gradually iteractive ultiedia replaced [3]. I recet years, face detectio techology teds to ature, the techology has bee widely used i caeras, coputer idetificatio syste ad iteractive advertisig. I [4], they research proposes a syste that egages audiece to the advertiseet through iteractive applicatios ad provides data to the advertiser/producer about their audiece, but we thik that is too coplex. Therefore, we propose a iteractive syste, usig text ad pictures to do the iteractive display, it ca allow users to quickly uderstad the cotets of advertiseets. 2. Iteractivity Kaba Advertiseet The Figure 1 is the cofiguratio of iteractive kaba advertiseet syste. At first, we capture the caera iage, the color segetatio extracts the ski color of a face fro a cluttered iage; the, biary iagig further fors a ore coplete regio. Next, orphological erosio eliiates soe of the sall spots i a tested iage. Cotrary to erosio, dilatio elarges ad coects a sall ad discoected, but arked, facial regio. Subsequetly, coected copoet labelig is eployed to ark ultiple faces i the iage. Fially, a area threshold ad a aspect ratio are used to validate the corrected facial regio. After the we use the Adaboost algorith [7-10] to ake face recogitio. At last, we ca judge by the distace betwee the captured face ad advertisig, whe the perso is closer ad closer, the words will accord to the distace for scalig to achieve Capture Iage Face Recogitio Iteractive Advertiseets Figure 1. Iteractivity kaba ad syste block diagra.

2 F.-Y. CHENG ET AL. 27 the purpose of attractig users. The Figure 2 is our hardware cofiguratio of capture iage, the caera outs o the Kaba Advertiseet above. 3. Face Recogitio The Figure 3 is our face recogitio syste, the face recogitio has three processig steps: Ski color detectio, Detect face regio, facial feature poits ad fially, through Adaboost screeig the ost right face of people. This part is aily to capture each perso s face for the iage Ski Color Detectio The color segetatio is a iportat pre-processig step i the face recogitio ethods. We are used HIS ethod [5] to detectio face ski color. Frist, we trasfor the iage of RGB three color chages to H, S. It ca be writte as, if B G H (1) 360 if B G where =cos 1 1 ( ) 2 RG RB 2 RG RBGB 1/2 (2) 3 S 1 i( R, G, B ) (3) RGB The we ca follow the rule to fid ski color f c : Kaba Advertiseet caera Figure 2. Iteractivity kaba ad syste cofiguratio. Figure 3. Face recogitio block diagra. f c 1, if 8 H 32 ad 30 S 163 (4) 0, otherwise After the ski color detectio, we oly see the portio of ski color, as show i Figure Face Regio Detectio Method After the Ski color detectio, we chage the RBG to biary used thresholdig. The, we cosulted the paper s ethod [6] to the biary iage is sub-divided ito blocks. The, the total ski area withi a block is coputed, ad if this is greater tha or equal to 40% of the block area, the block label is assiged to be ski. A coected regio step is the perfored by exaiig the 8 eighbourhood coectivity aog the blocks to create a set of cadidate regios. Face regios are selected aogst the cadidate regios as the regios havig a aspect ratio correspodig to the ratio, tha use the rate value of the iage to defie the threshold. It is showed i Figure Capture Facial Feature Poits Eyes Detectio It is obvious that eyes are o-ski color regios, the C r ad C b copoet of eyes ad ski cotais bigger differece i the YC r C b space, ad the C b is higher tha the C r i the eyes regio. It ca detectio locatio ad size by above iforatio Mouths Detectio Mouth is also o-ski color regio, i the outh the C r is uch higher ad the C b uch lower. Icreasig the differece betwee the C b ad C r ca accurately detected size ad locatio Adaboost Algorith Machie learig algorith is flourishig i recet year, widely used at various levels. Face Detectio this issue i order to obtai better characteristics also itroduces a achie learig cocepts, these studies are a breakthrough i the past to the face detectio frae, ost otably the 2004 study is preseted usig the itegral iage Viola for the characteristic value of the AdaBoost face detectio ethod. AdaBoost is a algorith for costructig a strog classifier as liear cobiatio of weak classifiers [7-10] Haar-Like Features ad Itegral Iage A set of Haar-like features, used as the iput features to the cascaded classifiers, are show i Figure 6. I our work, Haar-like features cosideratio is usig itegral iage to iprove coputatio efficiecy.

3 28 F.-Y. CHENG ET AL. (a) Figure 4. (a) Origial iage; (b) Ski color detectio. (a) Figure 5. (a) Morphological process; (b) Face regio detectio. Figure 6. The Haar-like features for AdaBoost algorith. The Haar-like features that show i Figure 6. It used i our face detectio syste. The features ca be rapidly coputed at differet scales by itroducig Itegral Iage AdaBoost Algorith I fact AdaBoost is a classificatio of cocepts, for exaple, I order we pick a better tha oral a little bit (>= 50%) of the algorith, it ca agai ad agai use update weightig approach to reduce error rate, The process is as follows 1) Iput M saple of the target iage ad N saple ot of the target iage, ad I search the uber of features. 2) Iitialize weights Target iage saples weights 1 2M (5) No-target iage saples weights 1 2N (6) 3) For each feature j, trai a weak classifier T, ad evaluate its error E with respect to W (b) (b) M N (7) 1 1 E (1 T ) T I this derivatio whe T = 1 cosistet with the iage features, T = 0 does ot eet. 4) Usig step 3. Add the choose features to the stage ad deterie the correspodig weights 1 E Wi log (8) E 5) For step 3. Searchig the better features to updates iage saple weights. Updated target iage saple weights. 1E E (9) 1 E Updated o-target iage saples weights. E 1 E 6) Noralize the weights M E (10) (11) (12) 7) Check whether the uber of the curret search features to eet the dead, if the lack of jup back to step 3, otherwise the ed of. 4. Iteractive Advertiseet Syste The Figure 7 is our iteractive advertiseet syste flow process. At first, we capture the caera s iage, Figure 7. Iteractive ad syste flow process.

4 F.-Y. CHENG ET AL. 29 the thought the iage to do face recogitio processig, to get the iforatio of people who watch the Advertiseet, ad the the syste further deterie whether capture face or ot, i other words, to deterie if soeoe is watch the kaba advertiseet syste, after the to detectio the distace betwee the perso s face ad advertiseet, whe people are approached the advertiseet, the advertiseet will also show ore essage tellig the people, let people ca lear ore about the details of the advertiseet, to icrease the ipressio of people watch advertiseet. 5. Result ad Discuss We use a 20 illio pixels webca ad a 36-ich TV to achieve the Iteractive Kaba Advertiseet Syste. The webca is set i place of 130c high ad agle of 90 degrees. The Figure 8 shows the iforatio that the relatioship betwee pixel size of face ad the distace fro perso to caera. Whe the syste captures the perso s face, we ca use the pixel size of face to deterie where the perso. The Figure 9 is the user iterface of progra, this progra of iterface ca divided ito two parts, the caera of iage is o the left, the iteractive kaba advertiseet is o the right. If soeoe walks past i frot of the kaba advertiseet, the syste will catch perso s face, ad the kaba advertiseet also shows soe words to attract people s attetio, it is like Figure 9. Whe perso is closer ad closer, the syste will calculate the face of pixels to detect the distace, if people rely o close eough, the syste will chage the adver- Figure 8. Graph of ratio betwee perso ad caera. Figure 9. The user iterface of progra. Figure 10. It chage the ad cotet whe people rely o close eough. tisig cotet, display ore iforatio attract people to cotiue to watch, it is like Figure Coclusios I this paper, we propose a Adaboost algorith approach to the face recogitio for applicatios of iteractive advertiseet syste. Although it has a lot of people to research for the subject so far, however, the proposed approach are ore coplex, build ay ore cost or eed to take the tie to iteract with advertiseet, caused people icoveiece. Our syste oly through the distace betwee the face ad advertisig to iteract, through words ad pictures to attract people, reduce the coplexity of the syste also allows people quickly to uderstad ore iforatio of advertiseets. REFERENCES [1] J. H. Cho, Y. J. Sah ad J. Ryu, A New Cotet-related Advertisig Model for Iteractive Televisio, Broadbad Multiedia Systes ad Broadcastig 2008, March April , pp [2] M.-H. Hsieh, D.-L. Yag ad J.-Y. Dai, A Face Recogitio Syste Prototype to Evaluate the Effectiveess of Digital Advertiseet, 2010 Coferece o Coputer Visio, Iage Processig ad Iforatio Techology, Zhogli, Taiwa, pp [3] J. Ki ad S. Kag, A Otology-Based Persoalized Target Advertiseet Syste o Iteractive TV, Cosuer Electroics (ICCE), 2011 IEEE Iteratioal Coferece, 9-12 Ja. 2011,pp [4] M. Taspiar, A. T. Naskali, M. Kurt ad G. Ere, The Iportace of Custoized Advertiseet Delivery Usig 3D Trackig ad Facial Recogitio, i Proc. The Secod Iteratioal Coferece o Digital Iforatio ad Couicatio Techology ad its Applicatios (DIC- TAP), 2012, pp [5] S. Guerfi, J.-P. Gabotto ad S. Leladais, Ipleetatio of the Watershed Method i the HSI Color Space for the Face Extractio, Advaced Video ad Sigal Based Surveillace, Sept. 2005, pp [6] M. Raha ad N. Kehtaravaz, Real-TieFace-Priorit y Auto Focus for Digital ad Cell-Phoe Caeras, IEEE Trasactios o Cosuer Electroics, Vol. 54, No. 4, 2008, pp doi: /TCE

5 30 F.-Y. CHENG ET AL. [7] J. X. Rua ad J. X. Yi Multi-Pose Face Detectio Usig Facial Features ad AdaBoost Algorith, Secod Iteratioal Workshop o Coputer Sciece ad Egieerig, 2009, pp [8] Y.-W. Wu ad X.-Y. Ai, Face Detectio i Color Iages Usig AdaBoost Algorith Based o Ski Color Iforatio, Workshop o Kowledge Discovery ad Data Miig, 2008, pp [9] S. A. Ialou ad S. Kasaei AdaBoost-Based Face Detectio i Color Iages with Low False Alar, Secod Iteratioal Coferece o Coputer Modelig ad Siulatio, 2010, pp doi: /iccms [10] Y. C. Xig, Z. Z. Wag ad W. P. Qiag, Face Trackig Based Advertiseet Effect Evaluatio, Iage ad Sigal Processig, CISP '09. 2d, 2009, pp. 1-4.