Hand Detections Based on Invariant Skin-Color Models Constructed Using Linear and Nonlinear Color Spaces

Hand detections based on two proposed pixel-based skin-color models are proposed. Different from the conventional methods, the proposed models are built on the invariant surface obtained by supervised learning on nonlinear color space which effectively preserves vital properties of skin colors in low illumination. By using a high-dimensional feature space, which incorporating probability of fitting the invariant surface with a nonlinear mapping value of chrominance in linear space, first skin-color model is proposed as a thresholding method according to feature values of pixels. Subsequently, a second skin- color model is constructed based on a parametric region which maps the learning data on an invariant surface. This mode achieves more favorable skin-color segmentation results than the conventional methods in experiments.