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.
Kernel-based head tracker for videophony
IEEE International Conference on Image Processing 2005 ; 3 ; III-1068
2005-01-01
293533 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Kernel-Based Head Tracker for Videophony
British Library Conference Proceedings | 2005
|HEAD MOTION TRACKER DEVICE AND HEAD MOTION TRACKER DEVICE FOR AIRCRAFT
Europäisches Patentamt | 2022
|High assurance head tracker monitoring and calibration
Europäisches Patentamt | 2020
|