A Morphological Approach for Human Skin Detection in Color Image

2 nd National Conference in Intelligent Computing & CommunicationOrganized by Dept. of IT, GCET Greater Noida, INDIAA Morphological Approach for Human SkinDetection in Color ImageLavanya Sharma D. K. Yadav Manoj KumarDept. of CSEMRCE Faridabad, INDIADept. of CSEMRCE Faridabad, INDIADept. of CSEMRCE Faridabad, INDIAshm.lavanya@gmail.com dileep.mrce@mrei.ac.in manoj.mrce@mrei.ac.inAbstractA reliable skin detection method that isadaptable to different human skin colorsand illumination is very essential forhuman skin detection. Today’s many skindetection methods are available inliterature but they are not able to cope withvariety of skin colors. Several imageprocessing and computer visionapproaches have been developed forhuman skin detection. In this work wepropose a mathematical morphology basedYCbCr method which given betterperformance as compare to other HSV,YCbCr color spaces. This method is simpleand efficient. The experimental resultshows that the proposed method canachieve satisfactory performance in realtime applications.Keywords- Image, Color space, HSV,YCbCr, MorphologyI. INTRODUCTIONHuman skin detection plays an importantrole in wide range of image processing andcomputer vision applications [1]. Skindetection using color information can be achallenging task as the skin appearance inimages is affected by various factors such asillumination, background, cameracharacteristics, and ethnicity [1, 2]. It is apreprocessing step in various fields such ashand detection and face detection, personalidentification, security systems, humancomputer interaction, human pose modeling,video conferences, face recognition,intelligent video surveillances, medicaldiagnosis like skin cancer detection, nakedpeople detection on internet and socialnetworking sites for the sake of contentfiltering [2, 3].In early application, skin detection was usedto detect anchors in TV news videos for thesake of automatic annotation in video,archival, and retrieval [3]. In such anapplication, it is very crucial that the faceand the hands of the anchor person are thelargest skin-tone colored region in a givenframe. Typically, news programs are shot inindoor controlled environments with manmadebackground that contains skin-colorobject [3, 4].Skin segmentation is a process ofdifferentiating skin region from non skinregion in color images [5]. Skin color isoften used because it is invariant toorientation, size and gives an extradimension compared to gray scale methodsand fast to process [5, 6]. Skin detection isused to determine the presence andlocalization of a face, hand or other bodyparts in an image by distinguishing it fromISBN: 9788175157538

2 nd National Conference in Intelligent Computing & CommunicationOrganized by Dept. of IT, GCET Greater Noida, INDIAbackground of image or from all otherpatterns present in an image [4, 6].Skin detection system is never perfect and isnot robust for dealing with some real-worldproblems. The main problems with therobustness of skin color detection are (i) itvaries from person to person (ii) dependenceon the illumination condition, and (iii) skincolor is not unique. Skin detector transforma given pixel into an appropriate color spaceand then use a skin classifier to label thepixel whether it is a skin or a non-skin pixel[3, 6, 7]. In this paper, we propose anefficient method for skin color segmentationon color images. In the first step color imagefrom input color space is converted to RGBcolor space and then converted into YCbCr.Single or multiple ranges of threshold valuesfor each color space components are definedin such a way that the image pixel value fallwithin these predefined range(s) are selectedas skin pixels [2]. Then we have appliedmorphological operations such as erosion toremove pixels from outside boundary orremove outliers and to separate skin regionand non skin region.II. COLOR SPACEIn literature different kinds of color spacesare available. Most of the research is basedon RGB and YCbCr color space. The firststep is to select a suitable color space whichcan easily differentiate between skin andnon skin pixels [5, 8]. This mainly affectsthe performance of skin detector and itssensitivity to change in illuminationconditions.2.1 RGB color spaceRGB represents the default color spacewhich is used for storing and representingdigital images using linear or non-lineartransformation of RGB, we can get anyother color space. The color spacetransformation is mainly used to reduce theoverlapping problem between skin and nonskinpixels [1, 5, 9]. The choice ofappropriate color space is often guided bythe skin detection methodology and theapplication. It is the most commonly usedcolor space but is not ideal for allapplication.Kovac et al. work within the RGB colorspace and deals with the illumination underwhich the image is captured [3]. Thereforewe classify skin color by heuristic rule thattakes into account two different conditions:uniform daylight and flash or lateralillumination [2, 5,10]. The skin color isdetected using following conditions atuniform daylight illumination [5].R > 95, G > 40, B > 20Max(R, G, B) -min(R, G, B) < 15|R – G| > 15, R>G, R>BORThe skin color at flashlight or daylightlateral illumination [5]R>220, G>210, B>170|R-G|

2 nd National Conference in Intelligent Computing & CommunicationOrganized by Dept. of IT, GCET Greater Noida, INDIAcommonly used in image processing as itseparates the luminance, in Y component,form the chrominance described through Cband Cr components. The Cb and Crcomponents are used to characterize the skincolor information. Chai and Ngan developan algorithm for spatial distribution ofhuman skin color. A skin color map isderived and used on chrominancecomponent of input image to detect skinpixels. Then some rules are set to reinforcethose regions of skin color pixels which arelikely belong to the facial regions [5, 9].Range for the skin color reference map is:77

2 nd National Conference in Intelligent Computing & CommunicationOrganized by Dept. of IT, GCET Greater Noida, INDIAbuilding a better preprocessing method forcolor skin detection in video.Fig. 2. (a) original image ( b) result using RGB colorspace (c) result using YCbCr color space (d) resultusing Proposed methodMain advantage of this method is that it hassmallest overlap between skin and non skinregion, simplicity and less computationalcost. The method described in this paperfinds it difficult to differentiate whenbackground color of image is similar to skincolor.V. CONCLUSIONIn this paper, the skin detection method isbased on RGB and YCbCr color spacemodel and has been proposed a morphologybased method for automatic detection ofhuman skin in image(s). As exhibited inexperiments, the proposed method performsbetter in terms of accuracy and information.The skin detection system in this paper dealswith the detection of skin region in YCbCrand RGB color space. In case of YCbCrluminance is not taken into binarysegmentation and noise removal methods.Skin detection can also be used as anefficient preprocessing filter to find potentialskin regions in color images prior toapplying more computationally expensiveface or hand detectors. The proposedmethod produces good result as compare toother. Our future work is focused onREFERENCES[1] Leyuan Liu, Nong Sang, Saiyong Yang and RuiHuang, “Real-Time Skin Color Detection underRapidly Changing Illumination Conditions” IEEETransactions on Consumer Electronics, 2011.[2] Wei Ren Tan, Chee Seng Chan, PratheepanYogarajah, and Joan Condell, “A Fusion Approachfor Efficient Human Skin Detection”, IEEETransactions on Industrial Informatics, 2012.[3] Ahmed Elgammal, Crystal Muang and Dunxu Hu,“Skin Detection - a Short Tutorial”, Springer-VerlagBerlin Heidelberg 2009.[4] Jones, M.J., Rehg, J.M. “Statistical color modelswith application to skin detection”, InternationalJournal of Computer Vision, 2002.[5] Franceska Gasparini, Raimondo Schattini, “ SkinSegmentation using Multiple Threshold”, universitadegli Studi di Milano-Bicocca , Via Bicocca degliArcimboldi 8,20126 Milano Italy.[6] Burger W. M. “Digital Image Processing, anAlgorithmic Introduction Using Java” Edition-1,Springer 2008.[7] Shin, M.C., Chang, K.I., Tsap, L.V., “Doescolorspace transformation make any difference onskin detection?” WACV ’02: Proceedings of theSixth IEEEWorkshop on Applications of ComputerVision, Washington, DC, USA, IEEE ComputerSociety 2002.[8] J. Kavoc, P. Peer and F. Solina, “ 2D versus 3Dcolor space face detection”, 4 th EURASIP conferenceon Video/Image Processing and multimediacommunications, Croatia, 2003.[9] D. Chai, K. N. Ngan, “Face segmentation usingskin colour map in videophone applications”, IEEETransactions on circuits and systems for videotechnology, 1999.[10] John Canny. “A computational approach to edgedetection”, Pattern Analysis and MachineIntelligence, IEEE Transactions on, PAMI, 1986.[11] P.Patil and Y.M Patil, “Robust Skin ColorDetection and Tracing Algorithm”, InternationalJournal of Engineering Research & Technology(IJERT), 2012, ISSN: 2278-0181.ISBN: 9788175157538