By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing recommendations. It studies latest 3D face processing ideas, together with recommendations for 3D face geometry modeling; 3D face movement modeling; and 3D face movement monitoring and animation. Then it discusses a unified framework for face modeling, research and synthesis. during this framework, the authors current new equipment for modeling complicated average facial movement, in addition to face visual appeal diversifications as a result of illumination and refined movement. Then the authors practice the framework to stand monitoring, expression acceptance and face avatar for HCI interface. They finish this ebook with reviews on destiny paintings within the 3D face processing framework. 3D Face Processing: Modeling, research and Synthesis will curiosity these operating in face processing for clever human laptop interplay and video surveillance. It features a entire survey on present face processing ideas, which could function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, on the way to gain extra complicated graduate scholars and researchers.
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Additional info for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
Feature points defined on texture map. We define thirty-five facial feature points on the face surface of the generic head model. If we unfold the face component of the head model onto 2D, those feature points triangulate the face mesh into several local patches. 3. The system calculates 16 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS the 2D positions of the remaining face mesh vertices on the range map by deforming the local patches based on the range data. By collecting the range information according to the positions of the vertices on the range map, the 3D facial geometry is decided.
Principal Component Analysis (PCA) [Jolliffe, 1986] is applied to learning MUs from the database. The mean facial deformation and the first seven eigenvectors of PCA results are selected as the MUs. 3. The top row images are the frontal views of the faces, and the bottom row images are side views. The first face is the neutral face, corresponding to The remaining faces are deformed by the first four MUs scaled by a constant (from left to right). The method for visualizing MUs is described in Section 5.
For compactness and usability, we propose to use an splinebased model similar to the spirit of the approach of Ezzat et al. , 2002]. The advantage of this approach is that it employs only a few key facial shapes estimated from training data. In order to get a smooth trajectory, we use NURBS (Nonuniform Rational B-splines) interpolation, which is easy to 30 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS learn and perform well in general conditions. We also utilize the statistics of the training data in a way that covariance of the key shapes are used as weights in NURBS.
3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing) by Zhen Wen