Improving Adaptive Skin Color Segmentation by Incorporating Results from Face Detection

The visual tracking of human faces is a basic functionality needed for human-machine interfaces. This paper describes an approach that explores the combined use of adaptive skin color segmentation and face detection for improved face tracking on a mobile robot. To cope with inhomogeneous lighting within a single image the color of each tracked image region is modeled with an individual, unimodal Gaussian. Face detection is performed locally on all segmented skin-colored regions. If a face is detected, the appropriate color model is updated with the image pixels in an elliptical area around the face position. Updating is restricted to pixels that are contained in a global skin color distribution obtained off-line. The presented method allows to track faces that undergo changes in lighting conditions while at the same time it provides information about the attention of the user, i.e. whether the user looks at the robot. This forms the basis for developing more sophisticated human-machine interfaces capable of dealing with unrestricted environments.