Spatial-based skin detection using discriminative skin-presence features

The skin probability maps are refined using discriminative features (DSPFs).Spatial analysis of skin pixels is more successful when performed in DSPF skin maps.The textural features are extracted from skin maps rather than from the grayscale. In this paper we propose a new method for skin detection in color images which consists in spatial analysis using the introduced texture-based discriminative skin-presence features. Color-based skin detection has been widely explored and many skin color modeling techniques were developed so far. However, efficacy of the pixel-wise classification is limited due to an overlap between the skin and non-skin pixels reported in many color spaces. To increase the discriminating power of the skin classification schemes, textural and spatial features are often exploited for skin modeling. Our contribution lies in using the proposed discriminative feature space as a domain for spatial analysis of skin pixels. Contrary to existing approaches, we extract the textural features from the skin probability maps rather than from the luminance channel. Presented experimental study confirms that the proposed method outperforms alternative skin detection techniques, which also involve analysis of textural and spatial features.