A Face Detection Method Based on Skin Color Model

Transcription

1 A Face Deecion Mehod Based on Skin Color Model Dazhi Zhang Boying Wu Jiebao Sun Qinglei Liao Deparmen of Mahemaics Harbin Insiue of Technology Harbin China Absrac Face deecion plays a very imporan role in paern recogniion and he precision of face deecion direcly affecs he following resuls. This paper proposes a fas and precise mehod of face deecion in complex background based on skin color model. Firsly we exrac skin color regions of he image. Then we process hem wih Morphologic processing mehod and roughly filering. A las we show a mehod o recognize face by geomeric feaure of eyes which is similar o he feaure of ellipse. Keywords: Face deecion Skin color model Geomeric feaure 1. Inroducion As one of he mos imporan visual pars in images and videos face plays an imporan role in he compuer vision paern recogniion and mulimedia echnology research. Wih he developmen of compuer science in he field of humancompuer ineracion as he key echnology of face informaion process face deecion has been a very acive research field. Therefore sudying face deecion echnology has very imporan meaning. There have been many face deecion mehods. For example Sirohey used he edge exraced image o heurisicly search face and mach ellipical edge mehod [1] ; Govindaraju Srihari and Sher achieved face locaion by using he deformable emplae o mach conour lines of head and face [] ; Marin Hunke designed a skin color model o characerize face color used a phoographic model o realize illuminaion compensaion and achieved face deecion by a face color classifier and a neural nework [3]. In recen years some ypical mehods have been developed such as Adaboos mehod [4] learning archiecure mehod based on sparse neural nework [5] Bayesian discriminaing feaures mehod [6] fas mehod based on Boosing [789] and here are more deailed inroducions abou face deecion in references [10~1]. Each mehod has is own characerisics some have high precision and some are fas algorihms. This paper proposes a face deecion mehod based on skin color model which can reconcile boh he algorihm speed and precision. Procedure: 1) Exrac he skin color regions by a skin color model ) Roughly file 3) Recognize face by geomeric feaure of eyes.. Region segmenaion based on skin color model Skin color feaure is mainly described by skin color model. I is an imporan feaure of human face in he decec mehod based on knowledge because i is independen of face deails adaps o he change of facial expressions and roaion 1

2 has high sabiliy and could be easily disinguished from mos backgrounds..1. Illuminaion compensaion [13] Skin color informaion ofen moves o some direcions from is original color which is affeced by ligh source or acquisiion equipmen ec. We have o process he image as follows o avoid color deviaion: Se he image size is M N all he pixels color are ri ( j) gi ( j) bi ( j ) i 1 M j 1 N. Firsly we obain he brighness informaion of each pixel ki ( j) 0.99 ri ( j) gi ( j) bi ( j) hen we sor ki ( j ) from big o small: k1 k km N. A las we ge he average k (sup k ) / of he former five i1 i percen according value. If is big enough we se he brighness of he image as a reference whie heir value are adjused o he maximum 55 he remained pixels are adjused by he proporion 55/ k. cx cy ecx 1.60 ecy.41. a5.39 b14.03 We will ge segmenaion resuls which is show in Fig.1. Fig.1 segmenaion resuls 3. Face deecion The procedure of face deecion algorihm is shown in Fig.... Skin color model selecing [14] By ransforming image o YCbCr space and combining Cb and Cr wih formula ( x ec x ) ( y ec ) y 1 a b x cos sincb cx y sin cos Cr c y we can judge i belongs o skin color space when he value is less han or equal o 1where Fig. Face deecion procedure

3 3.1. Morphological image processing I is very difficul o confirm a face because of a mass of discree poins in image over-segmenaion and some small holes in he region of eyes nose and mouse ec. afer region segmenaion. In his paper we apply dilaion erosion and seed filling mehods o overcome above problems. By making use of he operaor T D E E D o process segmened image we can pad small holes remove discree poins and horizonal noises as is shown in Fig.3.b) where D is he dilaion operaor E is he erosion operaor is he scale parameer and srucure elemen ( xy ) x y 1. Seed filling resul is shown in Fig.3.c). a) Segmened image Because face has a cerain size if he widh or heigh of one region is less han 19 pixels we will remove his region. Furhermore face has a cerain geomeric feaure ha is he raio of widh and heigh is close o 1. Bu in pracice process he raio is ofen no close o 1 herefore his proporion may be appropriaely expanded o [0.5]. We use his hreshold o filer remaining regions. The resul is shown in Fig.4. Fig.4 Roughly filered image 3.3. Human eyes locaion Gray image processing We ge original gray image according o Fig.4. Since eyes mus be in he firs half of face we preserve he firs half of possible region o reduce he amoun of compuaion and binarize hem. Firsly in order o remove small black poins we deal wih binary image by morphological processing. Then we rea possible regions by size and shape filers. A las according o characerisices of eyes we do some special processes as follows: 1) There are no black blocks under eyes wihin a cerain disance. ) The ceners of eyes are almos on horizonal line ha is deflecion angle is less han a cerain angle Verify eyes b) Processed by T c) Filled wih seeds Fig.3 Morphological processing 3.. Roughly filering According o he number m of possible eyes blocks we ake hree regulars o verify eyes: 1) If m we esablish wo pairs of concenric circles wih each black block s cener as he cener of circle and cerain lenghs as radiuses. For each block we calculae separaely he raio of pixels of skin color regions landed in he wo concenric circles. If he wo raios are larger han hreshold 0.8 he wo blocks are eyes. Oherwise we will cal- 3

4 culae he correlaion coefficien of hem as follows. ) If m we calculae he correlaion coefficien beween each block according o gray image as he following formula: Axy ( ) ABxy ( ) Bd AB max Axy ( ) A d Bxy ( ) B d where A is block-1 A is he gray average of A B is block- s reversal and ranslaion B is he gray average of B A B. If he correlaion coefficien of cerain wo blocks is he larges one of all correlaion coefficiens and no less han 0.5 we verify a pair of eyes. Oherwise we increase he hreshold of binarizaion. 3) If m 0 we go back o1) and increase he hreshold of binarizaion. If we find eyes in one region we can verify his region is face. If we find eyes in one region we can verify his region is face. 4. Resuls and analysis Experimenal plaform: AMD Sempron Processor GHz1.5GB Malab7.0. The experimenal images are all from Markus Weber s daase of California Insiue of Technology conaining 450 pieces 7 individual persons in differen brighness posure and background and he image size is in his paper. The average deecion ime is 1.513s. We successfully deeced 44 images 7 errors and leakily deeced 19 images. There are 13 images leakily deeced caused by he bad skin color segmenaion he remained 6 images and 7 errors are caused by inaccurae hreshold of binarizaion. Pars of resuls are shown in Fig.7. Fig.7 Pars of face deecion resuls 5. Reference [1] S. A. Sirohey Human Face Segmen aion and Idenificaion Technical Repor CS-TR-3176 Univ. of Maryland pp [] V. Govindaraju S. N. Srihari and D. B. Sher A Compuaional Model for Face Locaion IEEE Conf. on Compuer Vision Osaka Japan pp [3] H. Marin H. Hunke Locaing and Tracking of Human Faces wih Neural Neworks. Technical Repor of CMU pp [4] P. Vca M. J. Jones Robus Realime Face Deecion Inernaional Journal of Compuer Vision pp [5] M. H. Yang D. Roh and N. Abuja A Snow-based Face Deecor In: Solla SA Leen TK Muller KR eds. Advances in Neural Informaion Processing Sysems 1. Cambridge: MIT Press pp

5 [6] C. J. Liu A Bayesian Discriminaing Feaures Mehod for Face Deecion IEEE Trans. on Paern Analysis and Machine Inelligence pp [7] P. Viola M. Jones Rapid Objec Deecion Using a Boosed Cascade of Simple Feaures In: Kasuri R Medioni G eds. Proc. of he IEEE Compuer Vision and Paern Recogniion. Cambridge: IEEE Compuer Sociey pp [8] R. Xiao M. J. Li and H. J. Zhang Robus Mulipose Face Deecion in images IEEE Trans. on Circuis and Sysems for Video Technology pp [9] S. Z. Li L. Zhu Z. Q. Zhang A. Blake H. J. Zhang and H. Shum Saisical Learning of Muli-view Face Deecion In: Heyden A Sparr G Nielsen M Johansen P eds. Proc. of he 7h European Conf. on Compuer Vision. Cambridge: LNCS 350 Heidelberg: Springer-Verlag pp [10] L. H. Liang H. Z. Ai G. Y. Xu and B. Zhang A Survey of Human Face Deecion Chinese Journal of Compuers pp [11] W. Wang Y. S. Zhang and F. Fang Survey of Human Face Deecion and Recogniion Technology Journal of Hefei Universiy of Technology pp [1] C. X. Zhou J. Y. Yi Research on Face Deecion Chinese Science and Technology Informaion pp [13] Z. F. Liu Sudy on Face Deecion and Recogniion Docor's Academic Disseraion of Universiy of Sichuan pp [14] Rein-Lien Hsu Mohamed Abdel- Moaleb and Anil K.Jain Face Deecion in Color Images IEEE Trans. Paern Analysis and Machine Inelligence pp