The research on Face Alignment has both significant academic importance and wide applications. With the aim of designing an automatic Human-Computer interaction system based on face expression, we focus our research on the face detection, face alignment, feature representation, facial expression analysis and avatar control by facial expression. During the process, an in-depth study is made on face alignment. With consideration of face model and feature speciality, we incorporate feature combining and transforming strategy into the learning framework and searching process which dramatically improves the robustness and accuracy of alignment result. And the main contribution of this thesis includes: 1. With regard to the face alignment based on Active Shape Models, we proposed a combination feature of gray and edgeness which balances the global and local information. 2. With regard to the face alignment based on Active Appearance Models, we made improvement from five aspects. We proposed a combined Active Appearance Models whose texture feature is the combination of gray and edge information. We extended the combined Active Appearance Models to a weighted form, which enables us to employ a coarse-to-fine search strategy. Considering the face that different pixels in face area take different role of importance in the texture match process, we incorporated a weight-map into the Active Appearance Models. In order to minimize the error introduced during labeling training samples by hand, which dramatically affects the robustness of the texture model, we suggest a semi-automatic and multi-pass labeling strategy be employed to build a good texture model. In order to build a texture model less sensitive to light variation, we proposed a texture pre-process method which is subdividing the face area and then smoothing the sub-areas several times respectively. 3. Using the improved Active Appearance Models, we developed an avatar control system and in every aspect of the function of which, we proposed the best ways of implementation.