Universal Skin Detection Without Color Information

This paper concerns the detection of human skin in images. Researchers have demonstrated that color information alone is highly effective for skin detection and related segmentation tasks. However, many situations exist for which color information is either not available or not reliable. Examples include images obtained under low-light conditions, legacy grayscale images and videos, and near-infrared images. Furthermore, for human observers it is often the visual texture of skin that is prominent in an image. This paper introduces a novel context-aware framework for skin detection that targets grayscale images. The technique exploits prior geometric knowledge from face detection to localize a set of candidate skin patches in a given image. The system then uses texture-based features (local binary patterns, lacunarity) and statistics derived from grayscale intensities to identify new candidate regions of skin in the image. The system leverages the information resulting from superpixel segmentation to perform region growing. This paper demonstrates feasibility of the approach through experimental results using two standard databases. All the results and the source code will be publicly available 1.