By Ming-Hsuan Yang, Narendra Ahuja
3D Face Processing: Modeling, research and Synthesis will curiosity these operating in face processing for clever human desktop interplay and video surveillance. It encompasses a complete 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 that it will profit extra complex graduate scholars and researchers.
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Additional info for 3D Face Processing (The Kluwer International Series in Video Computing): Modeling, Analysis, and Synthesis
More recently, Romdhani and Vetter improved the efficiency, robustness and accuracy of the registration process in [Romdhani and Vetter, 2003]. , 2004]. Subspace modeling: When the 3D face database includes more geometry variations, PCA may no longer be a good way to model the face geometry subspace. Other subspace learning methods, such as Independent Component Analysis (ICA) [Comon, 1994], local PCA [Kambhatla and Leen, 1997], need to explored the find better subspace representation. Illumination effects of texture model: The textures of the 3D face models also need to be collected to model the appearance variation, as in [Blanz and Vetter, 1999].
For the adaptation, it requires that the face be in its neutral position in the first image 36 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS frame and the facial feature locations are detected. In the original system, it is already done interactively via a GUI. In the future, we plan to detect these feature locations automatically such that the whole fitting could be automatic. 1. Typical tracked frames and corresponding animated face models. (a): The input image frames. (b): The tracking results visualized by yellow mesh overlayed on input images.
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. Kshirsagar and Magnenat-Thalmann [Kshirsagar and Magnenat-Thalmann, 2000] also trained a three-layer neural network to classify speech segments into vowels.