By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing strategies. It studies current 3D face processing strategies, together with strategies 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 common facial movement, in addition to face visual appeal adaptations because of illumination and sophisticated movement. Then the authors practice the framework to stand monitoring, expression attractiveness 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 desktop interplay and video surveillance. It encompasses a finished survey on present face processing strategies, that could function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, that allows you to 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)
In the original system, L was manually designed using Bezier volume, and represented by the displacements of vertices of face surface mesh. The design process was labor-intensive. To derive L from the learned MUs in our current system, the “MU fitting” process described in Chapter 3 is used. 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.
2 shows three snapshots of a video that we used in our experiment. This video has 147 frames. 26L. Moreover, the hybrid coding results have much higher visual quality. Because our tracking system works in real-time, it could be used in a real-time low bit-rate video phone application. More details of the model-based face video coding application will be discussed in Chapter 9. , 2003]. The bimodal speech recognition system improves the speech recognition rate in noisy environments. The emotion recognition system is being used to monitor students’ emotional and cognitive states in a computer-aided instruction application.
1999]. Principal Component Analysis (PCA) [Jolliffe, 1986] learns orthogonal components that explain the maximum amount of variance in a given data set. , 2001, Reveret and Essa, 2001]. Then, arbitrary complex face deformation can be approximated by a linear combination of just a few basis vectors. , 2001b, Reveret and Essa, 2001]. Furthermore, facial deformation is known to be localized. To learn a localized subspace representation of facial deformation, Non-negative Matrix Factorization (NMF) [Lee and Seung, 1999] could be used.