Skin Color Segmentation Using Coarse-to-Fine Region on Normalized RGB Chromaticity Diagram for Face Detection

Abstract

Aryuanto SOETEDJO•õa), Nonmember and Koichi YAMADA•õ•õb), Member SUMMARY This paper describes a new color segmentation based on a normalized RGB chromaticity diagram for face detection. Face skin is extracted from color images using a coarse skin region with fixed boundaries followed by a fine skin region with variable boundaries. Two newly developed histograms that have prominent peaks of skin color and nonskin colors are employed to adjust the boundaries of the skin region. The proposed approach does not need a skin color model, which depends on a specific camera parameter and is usually limited to a particular environment condition, and no sample images are required. The experimental results using color face images of various races under varying lighting conditions and complex backgrounds, obtained from four different resources on the Internet, show a high detection rate of 87%. The results of the detection rate and computation time are comparable to the well known real-time face detection method proposed by Viola-Jones [11], [12].

Eye Detection Based-on Color and Shape Features

“…From the figure, it could be seen that the method also extracts non-white objects outside the face. It is confirmed by [19] and could be observed from Fig. 4 that the above formulas will not extract skin color.…”

“…Color thresholding is usually used for skin color segmentation [13], [19]. Previous work [19] shows that the normalized RGB color space is effectively used for skin color segmentation.…”

“…Previous work [19] shows that the normalized RGB color space is effectively used for skin color segmentation. Skin region is defined on the normalized RGB chromaticity diagram as shown in Fig.…”

Segmentation of Road Guidance Sign Symbols and Characters Based on Normalized RGB Chromaticity Diagram

“…In our previous works [14,15], we proposed a color segmentation technique based on the normalized RGB chromaticity diagram for detecting red color sign [14] and detecting human skin color [15].…”

“…This line is determined by the following equation TB r g (4) where TB is the intersection of the line with g-coordinate. TB is calculated automatically by analyzing the peaks/valleys of the newly developed histogram called g_neg histogram [15]. The g_neg histogram is created by counting pixels with the value obtained by adding g value and r value (g + r).…”