Evaluation of an appearance-based 3D face tracker using dense 3D data

The ability to detect and track human heads and faces in video sequences can be considered as the finest level of any video surveillance system. In this paper, we introduce a general framework for evaluating our recent appearance-based 3D face tracker using dense 3D data. This tracker combines online appearance models with an image registration technique and can run in real-time and is drift insensitive. More precisely, accuracy and usability of this developed tracker are assessed using stereo-based range facial data from which ground truth 3D motions are computed. This evaluation quantifies the monocular tracker accuracy, and identifies its working range in 3D space. Additionally, this evaluation gives some hints on how the tracker can be fully exploited.