Tracking regions of human skin through illumination changes
New human computer interfaces are using computer vision systems to track faces and hands. A critical task in such systems is the segmentation. An often used approach is colour based segmentation, approximating the skin chromaticities with a statistical model, e.g. with mean value and covariance matrix. The advantage of this approach is that it is invariant to size and orientation and fast to compute. A disadvantage is that it is sensitive to changes of the illumination, and in particular to changes in the illumination colour. This paper investigates (1) how accurately the covariance matrix of skin chromaticities might be modelled for different illumination colours using a physics-based approach, (2) how this may be used as a feature to classify between skin and other materials. Results are presented using real image data taken under different illumination colours and from subjects with different shades of skin. The eigenvectors of the modelled and measured covariances deviate in orientation about 4. The feature to distinguish skin from other surfaces is tested on sequences with changing illumination conditions containing hands and other materials. In most cases it is possible to distinguish between skin and other objects.
tracking physics based vision colour analysis skin-colour modelling varying illumination