Kernel-based head tracker for videophony

An approach for automatically segmenting and tracking a face in a sequence of color images is presented. The face detection in the initial image frame consists of a two-step process: the face candidates selection, using skin color clustering, and the face verification, yielding the best face candidate based on shape and color cues. The tracking of the head in the subsequent frames is performed via a kernel-based method wherein a joint spatial-color probability density characterizes the head region. In this context, the novelty of our tracking approach lies in the introduction of two parametric models: a geometric transformation enabling the rotation, scaling, and translation of the target, and an affine illumination change model. The parameters of these models are estimated by minimizing the similarity between the predicted and the current head appearance. The proposed algorithms achieve reliable detection and tracking results.