Luminance adapted skin color modeling for the robust detection of skin areas
Statistical models of color channels have been used for the detection of skin areas. However, since the distribution of colors also change as the luminance varies, color distribution models without considering the luminance variation do not work well for the images taken under various illumination conditions. Hence we propose a new skin detection algorithm that considers the luminance value in modeling the color distribution. For implementing this idea, we need a sample of skin color in the image, which can be obtained from the face. It is noted that the faces can be detected without color feature, but by using only structural features such as eyes, mouth, etc. In our algorithm, the eye detector and elliptical boundary are used to detect the face. From the face region, joint Gaussian distributions of color components with respect to the luminance values are obtained. Then, each pixel in the image is classified into skin or non-skin using the statistics obtained from the face region. Experimental results show that the proposed skin color model outperforms the conventional methods in terms of the detection accuracy and F-score.