Skin Detection applied to Multi-racial Images Skin detection based on mixture of color spaces

Jouglas Alves Tomaschitz PUCPR Pontifícia Universidade Católica do Paraná

Curitiba-Pr, Brazil jouglas.t@gmail.com

Jacques Facon PPGIA

PUCPR Pontifícia Universidade Católica do Paraná Curitiba-Pr, Brazil

facon@ppgia.pucpr.br

Abstract— A skin detection methodology based on color skin clusters is proposed. The paper shows that, by adequately mixing color spaces, it is possible to segment the human skin, even if the images are collected from many different sources. Experimental results have shown the efficiency of the methodology in skin detection from different ethnics.

Keywords- Skin, Segmentation, Color Space, rgb, HSV, TSL.

I. INTRODUCTION Image segmentation based on skin color is still an important

task. Several applications, such as Video-surveillance, Illicit Content Detection, Medical Analysis, Pose Interpretation need to preliminary detect human skin.

The aim of skin segmentation is to determine if a color pixel is a skin color or non skin color for any human skin (Asian, Caucasian, Black etc…).

A wide variety of studies and different approaches have been proposed on skin color modeling, segmentation and recognition. The three fundamental approaches to skin segmentation available in the literature can be classified as non parametric, parametric and “direct skin cluster”. Non parametric skin methods are based on training processes. In parametric methods, Gaussian models are very often used and skin distribution is modeled by a Gaussian probability density function. The direct skin cluster approaches build a skin cluster by using color spaces.

A methodology focusing skin detection to mixed people images (Asian, Caucasian, Black etc…) is presented in this paper. By using few color spaces (rgb, HSV, TSL), we will show that it is possible to locate skin regions based on mixture of color spaces without previous skin and non skin classification. Tests carried out onto images collected from different sources and ethnics have shown the efficiency of the methodology.

The paper is organized as follows. Section 2 describes some color spaces. Section 3 presents the color space mixture. Experimental results over human images from different sources and ethnics are discussed in Section 4.

II. COLOR SPACE-BASED SKIN DETECTION Even if RGB space is one of the most common color

spaces, the RGB components are not directly used in skin detection. The main reasons are the great variation of skin tones and the sensibility of RGB space to the lighting conditions. Consequently, a wide variety of color spaces have been proposed and applied to the problem of human skin detection. Among the most important, we can cite [1] which have studied the color representation and quantization applied to skin segmentation. In [2] the authors have widely compared color spaces and have concluded that normalized r-g and CIExy spaces are the most efficient for skin segmentation. [3] have reviewed most widely used color spaces (HSI, HSV, HSL, YCrCb, TSL) and methods in skin detection and have concluded that TSL space is the most suitable to face skin-based segmentation. In [4] known-less color spaces have been reviewed (LUX, Log Opponent). In [5] the authors have shown that TSL space is very effective for skin segmentation when using a Gaussian model.

From a initial study of most relevant important color spaces applied to skin segmentation, rgb (or normalized RGB), HSV and TSL color spaces have been selected and used in this methodology.

A. rgb Space The rgb space is obtained by normalizing R, G and B

components. The great advantage of rgb normalization is the reduction of lighting effects. Some variations of normalization are available in the literature. The [6]´s normalization was used using the following formulation:

r = cR / (R+G+B), (1) g = cG / (R+G+B) b = cB / (R+G+B) , where r+g+b=1 and c a constant (here c=100).

B. HSV Space Based on human color perception, HSV (Hue-saturation-

Value) became a popular color space. While Hue is generally related to the light wavelength, Saturation measures the “colorfulness”. The transformation of RGB space to HSV one is obtained by the following formulation:

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C. TSL Space

After the studies of [5] and [3] , the TSL (Tint, Saturation, Luminance) space became more popular and applied in several studies. TSL space is a transformation of rgb space into more intuitive values, close to Hue and Saturation. Tint is the mixture of a color with white. The TSL transformation is obtained by:

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And r = R/(R+G+B), g =G/(R+G+B), b =B/(R+G+B) D. Color Space Mixture

Several color space combinations have been proposed in the literature to the problem of human skin detection.

Wang and Yuan [6] have purposed the statistical range in rgb space r ∈ [36, 46.5] , g ∈ [28, 36.5] and in HSV one H ∈ [0, 50], S ∈ [0.2, 0.68], V ∈ [0.35, 1].

[7] have concluded in HSV space that an efficient combination is H ∈ [0, 50] ∪ [340, 360], S ∈ [0.2, 1], V ∈ [0.35, 1].

[8] have proposed in HSV space H ∈ [0, 25] ∪ [335, 360], S ∈ [0.2, 0.6], V ∈ [0.4, 1]. Where H range is more to red colors and S range excludes pure or dark red.

Sobottka and Pitas [9] have obtained good results applying the following range H ∈ [0, 50], S ∈ [0.23, 0.68] with no limitation on the V value.

Garcia and Tziritas [11] have succeeded in skin segmentation by using:

S ≥ 10 ; V ≥ 40 V ; S ≤ 110 - H - 0.1V or

H ≤ 75 - 0.4V; Nabiyev and Günay [10] have proposed in TSL space: T

∈ [0.4, 0.6], S ∈ [0.038, 0.25], L ≥ 80.

III. COLOR SPACE MIXTURE

The proposed methodology for skin detection is based on mixture of color spaces aiming an efficient segmentation of human skin.

A. HSV and rgb By considering the previous studies, we decided to better explore the advantages of HSV and rgb spaces in reducing the illumination influence. The proposed ranges are:

r ∈ [38, 55] ; g ∈ [25, 38] ; (4) H ∈ [0, 50] ∪ [340, 360]; S ≥ 0.2 ; V ≥ 0.35.

This combination has proved to be efficient in excluding nonskin regions that look like skin in different illumination situations.

B. HSV and rgb and TSL By adding TSL space, we propose the following ranges:

r ∈ [38, 55] ; g ∈ [25, 38] ; (5)

H ∈ [0, 50] ∪ [340, 360]; S ≥ 0.2 ; V ≥ 0.35 T ∈ [0.4, 0.6]; S ∈ [0.4, 0.6], L ≤ 80

IV. EXPERIMENTAL RESULTS

The color space mixtures presented above have been applied onto multi-racial images collected from different sources (mainly web). Fig. 1-(a) represents a multi-racial family picture. As expected, by using rgb space, the result is efficient (Fig. 1-(b)). But some no skin regions (glasses, hair) have been segmented as skin. Results obtained from HSV and TSL spaces are similar and not very efficient (Fig. 1-(c) and (d)). The HSV-rgb and HSV-rgb-TSL combinations (Fig. 1-(e) and (f)) have succeeded in segmenting a mixture of Asian, Caucasian and Black skins and excluding glass and hair regions.

More complex skin segmentation has been applied onto the poster illustrated in Fig. 2-(a). In spite of complexity of the poster (variability of picture size, color, background), it is possible to observed (Fig. 2-(e) and (f)) the efficiency in segmenting human skins by mixing rgb, HSV and TSL ranges.

V. CONCLUSIONS A methodology based on mixture of color spaces to

segment human skin in multi-racial images was proposed. By mixing few color spaces as rgb, HSV and TSL ones, it is possible to segment Asian, Caucasian, Black and Indian skins without previous skin and non skin classification.

Original Image rgb segmentation

HSV segmentation TSL segmentation

HSV-rgb segmentation HSV-rgb-TSL segmentation

Figure 1. Skin segmentation results onto family picture

REFERENCES

[1] Phung, SL, Bouzerdoum, A, Chai, D, “Skin segmentation using color pixel classification: analysis and comparison”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), pp 148-154, January 2005.

[2] Terrillon, J.-C., Pilpré A., Niwa Y. and amamoto K., “Analysis of Human Skin Color Images for a Large Set of Color Spaces and for Different Camera Systems”, MVA 2002 IAPR Workshop on Machine Vision Applications, pp 20-25, 2002

[3] Vezhnevets V., Sazonov V., Andreeva A., "A Survey On Pixel-Based Skin Color DetectionTechniques". Proc. Graphicon-2003, pp 85-92, Moscow, Russia, September 2003.

[4] Mortiz Störring , “Computer Vision And Human Skin Colour “, Phd Dissertation, Faculty Of Engineering And Science Aalborg University, 177 Pages, 2004

[5] Terrillon, J.-C., Shirazi, M. N., Fukamachi, H., And Akamatsu, S., “Comparative Performance Of Different Skin Chrominance Models And Chrominance Spaces For The Automatic Detection Of Human Faces In Color Images”, Proc. Of The International Conference On Face And Gesture Recognition, pp 54–61, 2000.

[6] Plataniotis, K.N., Venetsanopoulos, A.N., “Color Image Processingand Applications”, Springer, Berlin, New York, 2000.

[7] Wang, Y., Yuan, B., “A novel approach for human face detection from color images under complex background” Pattern Recognition 34, pp 1983–1992, 2001.

[8] Herodotou, N., Plataniotis, K.N., Venetsanopoulos, A.N., “Automatic location and trackingof the facial region in color video sequences”, Signal Process., Image Comm. 14 (5), pp 359–388, 1999.

[9] Tsekeridou, S., Pitas, I., “Facial feature extraction in frontal views usingbiometric analogies”, Proc. EUSIPCO98, Rhodes, Greece, September 8–11, 1998.

[10] Sobottka, K., Pitas, I., “A novel method for automatic face segmentation, facial feature extraction and tracking”, Signal Process., Image Comm. 12 (3), pp 263–281, 1998

[11] V. Nabiyev, A. Gunay. “Towards a biometric purpose image filter according to skin detection”, The Second International Conference on Problems of Cybernetics and Informatics, pp 1-4, 2008

[12] Garcia, C., Tziritas, G., “Face detection using quantized skin color regions merging and wavelet packet analysis”, IEEE Trans. Multimedia 1 (3), 264–277, 1999.

(a) Original Image

(b) rgb segmentation

(c) HSV segmentation

(d) TSL segmentation

(e) HSV-rgb segmentation

(f) HSV-rgb-TSL segmentation

Figure 2. Skin segmentation results onto composed poster