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3D face analysis has been researched intensively in recent decades. Most 3D data (so called range facial data) are obtained from 3D range imaging systems. Such data representations have been proven effective for face recognition in 3D space. However, obtaining such data requires subject cooperation in a constrained environment, which is not practical for many real applications of video surveillance. It is therefore in high demand to use regular video cameras to generate 3D face models for further classification. The goal of our research is to develop a method of tracking feature points on a face in multiple views in order to build 3D models of individual faces. We proposed a three-view based video tracking and model creation algorithm, which is based on the Active Appearance Model and a generic facial model. We will describe how to build useful individual models over time, and validate the created dynamic model sequences through the application of face recognition. Tracking multiple view fiducial points of a face in a time sequence can also be used for facial expression analysis. Our experiments demonstrated the feasibility of the proposed work.