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IJAIS Word Template International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 2– No.2, May 2012 – www.ijais.org Survey on Skin Tone Detection using Color Spaces C.Prema D.Manimegalai AP/Head of CSE Department Prof/Head of IT Department JACSI College of Engineering , Nazareth, Tamil National Engineering College, Kovilpatti, Tamil Nadu,India. Nadu,India . ABSTRACT the output will be a skin and non skin. Classification like this Skin is arguably the most widely used primitive in human is commonly referred to as ‘pallet reorganization’. So skin image processing research and computer vision with detection is a problem of pattern recognition. Three major application ranging from face detection and person tracking to approaches are used in pattern recognition such as statistical, pornography filtering. It has proven to be useful and robust neural and symbolical. Among various frameworks, statistical cue for detecting human parts in images since (i) it is invariant approach has been studied and most widely used in skin to orientation and size (ii) it gives extra dimension compared to gray scale methods and (iii) it is fast to process. The main detection based on color information. problems with the robustness of skin color detection are The rest of this paper is arranged as follows. Section 2 however depends on illumination condition, it varies between describes the different color spaces used for skin detection. individuals, many everyday life objects are skin color like and Section 3 covers the existing skin color modeling methods. skin color is not unique. Environments comprehensive survey Comparison and discussion is given in section 4 and in this topic is missing. The work presented in this paper is a conclusion is drawn in section 5. survey of the most frequently used methods and techniques and their numerical evaluation results. 2. SKIN COLOR REPRESENTATION In image processing and computer vision, color can be used as General Terms a powerful descriptor to identify objects (or) extract features. Image Processing, Face Detection. Yet, color remains challenging to describe. Several color representations have been proposed. Each has its own Keywords Color Space information, Color Transform, Image advantages and reasons for being used in practice. This Segmentation, and Skin Detection. section reviews the most widely used color spaces for skin detection and their properties. 1. INTRODUCTION Human skin detection is most import in numerous 2.1 Basic Color Spaces applications such as facial analysis, gesture analysis, video 2.1.1. RGB surveillance, human machine interface, cyber crime RGB is the most commonly used color space to represent prosecution, image content filter, content aware video digital images, since most of the image display devices have compression, annotation, color balancing applications and some sort of RGB output. It is being used in every computer image retrieval. Skin detection means detecting image pixels and regions that systems as well as videos, cameras etc [41]. Moreover RGB contain skin tone color. Instead of using feature based face benefits from its simplicity and easiness of implementation. It detection methods, using skin color for detection have gained is not a favorable choice for color analysis and color based strong popularity. Skin detection using color information can reorganization system because of its high correlation between be seen from two points: one is a problem of ‘color the channels, significant perceptual non uniformity, mixing of classification’ and other is a problem of ‘color image chrominance and luminance data.[2]. This color space was segmentation’. used in [12], and [13]. In this paper, pixel based skin detection methods are discussed. In pixel based method, each pixel is classified as 2.1.2. Normalized RGB skin and non skin individually, independently from its Normalized RGB is obtained by normalizing RGB value to neighbors. Color based methods fall on this category. In their first normalization using the following equation. contrast, region based method is to take the spatial arrangement of skin pixels into account during the detection r= R/R+G+B , g=G/R+G+B, b=B/R+G+B. (1) stage to enhance the methods performance. Additional knowledge such as texture etc is required. Region based It is actually to reduce the dependencies of RGB values on methods are built on top of the pixel based ones. So this paper changing its illumination i.e. to separate luminance from focuses on pixel based approaches only. It is a standard binary chrominance. It is obvious that-(r+g+b=1) and by having any classification problem where the input is a color vector and two components of r, g and b, third component can be obtained. Some authors [5] and [42] used r-g component as a 18 International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 2– No.2, May 2012 – www.ijais.org sort of luminance. Separating luminance from chrominance is TSV and TSL-Tint, Saturation and value (Lightness) were the desired goal for color space detection. On account of this used in skin detection [15]. These spaces are more complex advantages and simplicity in transformation, n-RGB gains alternative to HSV. popularity among researchers. 1 2.1.3: CIE XYZ arctan r' 2 3 The CIE system describes color as a luminent component ifg ' 0 and two additional components X and Z. CIE-XYZ values g' 4 were constructed from psychophysical experiments and T ` 0ifg ' 0 (5) correspond to the color matching characteristics of human visual system. 1 arctan r' 1 2.2 Perceptual Color Spaces 2 ifg ' 0 (HIS,HVS,HSL,TSL) g' 4 2.2:1: HSV HSV- Hue, Saturation and value was first introduced by Alay Where r’=r-1/3,g’=g-1/3 Ray Smith in 1978. V is the level of brightness. This color model is a simple and linear transformation from RGB. Hue defines the dominant color [such as red, purple and yellow] of 9 an area. Saturation measures the colorfulness of the area in S r' 2 g' 2 (6) proportion of the brightness of the image. The conversion of 5 RGB to HSV is given by the following equations. Note that there is no standard way to convert RGB to HSV. R G B V & L 0.299R 0.587G 0.114B HiifB C 3 H (2) 360 HiifB C (7) Where TSL is really best choice of color space for ‘Gaussian Skin 1 color Modeling’ [2]. 2R G R B 2.3 Orthogonal Color Spaces Hi arccos (YC C ,YIQ,YUV,YES) R G 2 R G G B b r This color space i.e. YCbCr is commonly used by European TV system. Luminance (Y) and chrominance (C and C ) are b r used to represent this color space. max( R,G, B) min(R,G, B) Y= 0.299R+0.587G+0.114B. (8) S (3) max( R,G, B) Cb= B-Y (9) Cr= R-Y. (10) max( R,G, B) V (4) In this color space, chrominance and luminance are explicitly 255 separated. This property and its simplicity made this color The separability of chrominance and luminance is achieved in space is one of the most popular color space used in skin this space. Some other important properties are invariant to detection [17], [9]. Other Orthogonal color spaces such as highlight, surface orientation etc. These properties make this color space popular on their color segmentation [5]. YCgCr was used in [10]. YUV in [44], YES in [45] and YIQ in [12]. Some undesirable features of this color space noted including hue discontinuities and the compilation of brightness were 2.4Perceptually Uniform Color Spaces found which conflicts badly with the properties of color vision. 2.4.1: CIE-XYZ and CIE-xyY. Qiong Liu et al [28] used HS color space for skin detection. This representation is one of the first mathematically defined Their algorithm detects faces with different sizes, rotations color spaces which is based on additive color models. Colors and expressions under different illumination conditions fast are described by three imaginary primitives X, Y and Z. and accurately. Normalized values can be obtained by X Y Z x , y , z 2.2.2: TSV and TSL X Y Z X Y Z X Y Z (11) 19 International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 2– No.2, May 2012 – www.ijais.org In CIE-xyY , Y is also normalized from 0 to 100. 2.4.2: CIE CUV and CIE LAB (12) These are the non linear transformation of CIE Yxy. CIE This method is most obvious method because of its simplicity CUV is offering a much better perceptual uniformity which has attracted by many researchers [43]. I component in compared to its predecessors. YIQ space is also used for detecting skin pixels in which yellow people are present. The range of I component RI= [0, Both are device independent color spaces. Luminance and 50] [44]. HS space is used to detect skin. The range HS of chrominance are very well separated. Finally both are RH=[0, 50] and RS=[0.23, 0.68] are defined as skin pixels. reasonably perceptually uniform color system. These are not Wang & Yuan [45] have concluded in HSV space that an favorable in skin detection of its complexicity and efficient combination is computational expensiveness. These color spaces were used in [15] and [4]. (13) 2.4.3: Specific Color Space Sobottka I Pitas [44] have obtained good results by applying It has been observed that skin contains a significant level of the following range red.
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