Approach of facial feature extraction for 3-D face modeling

To create a friendly human interface for visual communication has become a popular and significant topic. One principal issue is to construct a three-dimensional (3-D) face model as a visual representation. A 3-D face model can provide the desired 3-D facial images related to a specified person by using the realistic 3-D structure and texture description. In order to automatically model the human face, we propose a coarse-to-fine method to extract the facial features from a 2-D head-and-shoulder color image and map these facial features to the 3-D template model. A skin-color-based scheme is first adopted to extract salient regions for selecting the face region. The salient region feature is invariant to different situations such as scaling, rotation, and skewing. To more precisely extract the facial features, face geometry and proportions obtained by training database are used for reference. After locating desired regions such as eyes, lip, nose and eyebrows, the corresponding facial features in the located regions are extracted based on the feature detection method, where a morphology-based scheme is used to enhance the corner features. Finally the complete facial features are mapped onto the adopted shape model and each facial feature is adjusted automatically to the proper position according to the training data. Our experiments demonstrate the feasibility of the proposed approach.