Automatic fitting and tracking of facial features in head-and-shoulders sequences

Model-based video coding requires the application of both image processing and machine vision techniques for proper fitting of the semantic model and its subsequent tracking throughout the rest of the sequence of a certain type (e.g. 'head-and-shoulders' or 'head-only'). A method of automatic semantic wire-frame fitting and tracking based on principal component analysis using an independent reference data-base of facial images is presented. The method has been tested on widely used 'head-and-shoulders' video sequences with very good results. It was possible to accurately retrieve the position of the desired facial features in all cases. The position of the facial features in initial frames was subsequently used in automatic tracking. Experimental results are also presented.