By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing suggestions. It stories latest 3D face processing ideas, together with suggestions 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 tools for modeling advanced normal facial movement, in addition to face visual appeal diversifications because of illumination and sophisticated movement. Then the authors practice the framework to stand monitoring, expression popularity and face avatar for HCI interface. They finish this e-book 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 machine interplay and video surveillance. It includes a finished survey on current face processing strategies, that may function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, so one can profit extra complicated graduate scholars and researchers.
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Extra resources for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
We impose the non-negativity constraint in the linear combination of the facial motion energy. edu (under category “Computational Neuroscience”). The algorithm is an iterative optimization process. In our experiments, we use 500 iterations. 4(a) shows some parts derived by NMF. Adjacent different parts are shown in different patterns overlayed on the face model. We then use prior knowledge about facial muscle distribution to refine the learned parts. The parts can thus be (1) more related to meaningful facial muscle distribution, (2) less biased by individuality in the motion capture data, and (3) more easily generalized to different faces.
The estimations are then nonlinearly weighted to produce the final visual estimation. The Gaussian mixture approach produces smoother results than the vector quantization approach. However, neither of these two approach described consider phonetic context information, which is very important for modeling mouth coarticulation during speech. Neural network based approaches try to find nonlinear audio-to-visual mappings. Morishima and Harashima [Morishima and Harashima, 1991] trained a three 44 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS layer neural network to map LPC Cepstrum speech coefficients of one time step speech signals to mouth-shape parameters for five vowels.
Fua and Miccio [Fua and Miccio, 1998] developed system which combine multiple image measurements, such as stereo data, silhouette edges and 2D feature points, to reconstruct 3D face models from images. Because the 3D reconstructions of face points from images are either noisy or require extensive manual work, researcher have tried to use prior knowledge as constraints to help the image-based 3D face modeling. One important type of constraints is the “linear classes” constraint. Under this constrain, it assumes that arbitrary 3D face geometry can be represented by a linear combination of certain basic face geometries.