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For example (KemelmacherShlizerman & Basri 2011), and (Jose et al. 2010) urged that learning a generic 3D face model requires large amounts of 3D faces. However, this issue has not been statistically analyzed in terms of representational power of the model, to the best of our knowledge. Although (Iain et al. e. Active Appearance Model (AAM) and 3DMM, they did not conduct any statistical analysis or testing on the two models. (Volker & Thomas 2003). The generation of synthetic views from 2D input images was not needed.

A basic assumption is that any 3D facial surface can be practically represented by a convex combination of shape and texture vectors of 100 3D face examples, where the shape and texture vectors are given as follows: Then the mean of the RP "RP-mean" for all test faces is used to represent the RP of the model. Figure 1 shows the relationship between the sample size and RP-mean. It shows that there is exponential relationship between the sample size and the RP-mean. An exponential regression model is applied to fit the curve in Figure 1, as follows Information from each of these exemplar heads was saved as shape and texture vectors (Si , Ti ) where i = 1, .

2008). Brno University of Technology at TRECVid 2008. In Surveillance Event Detection Pilot. gov/projects/trecvid/. Dalal, N. and B. Triggs (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition (CVPR), pp. 886–893. Dikmen, M. et al. (2008). IFP-UIUC-NEC at TRECVid 2008. In Surveillance Event Detection Pilot. gov/projects/trecvid/. Guo, J. et al. (2008). Trecvid 2008 event detection by MCG-ICT-CAS. In Surveillance Event Detection Pilot. gov/projects/trecvid/.

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