Image Analysis for Markes-less Facial Tracking

Abstract: Tracking of facial features is increasingly used in game and film industry as well as for other applications. Most tracking systems are currently using markers which unfortunately are tedious and cumbersome. Marker-less facial tracking is supposed to eliminate the disadvantages of the marker-based approaches. This thesis investigates different algorithms for marker-less tracking and presents how to apply them in a robust way. View-based and component sensitive normalized face images can achieve accurate tracking results based on the Active Appearance Algorithm. Post processing the parameters of global motions of the model smoothes the synthesized video sequence. Tacking results for faces and a tool developed for creating training images are also presented. Key Words: Marker-less, Texture registration, Active Face Model, Active Appearance Algorithm, View-based texture model, Component sensitive, Post-processing.