Progressive AAM and Multi-band modeling

1. Progressive AAM and Multi-band modeling 报告人：崔 滢 2011年12月16日

2. Progressive AAM Multi-band modeling of Appearance Experiments Active Appearance Model can be represented using a common parameter vector c as follows:

3. Progressive AAM The progressive AAM consists of two stages as in the basic AAM: Modeling stage and Face fitting stage. Modeling and relation derivation stage : a) Firstly, construct the inner face AAM model from the inner facial feature points of the model faces, which are more stable and less variant than outer parts of the face. b) Secondly, construct the whole face AAM model from the whole facial feature points of the model faces. c) Thirdly, derive the relation between the parameter vector of the inner face AAM model and the parameter vector of the whole face AAM model.

4. Progressive AAM The progressive AAM consists of two stages as in the basic AAM: Modeling stage and Face fitting stage. 2) Face Fitting stage: a) Fit the inner face AAM model into a new incoming face, that is, find the inner face AAM parameter vector fitting the inner face AAM model. b) Estimate the initial values for the parameter vector of the whole face AAM fitting by using the inner face AAM parameter vector obtained in 1)-c). c) Fitting the whole face AAM model into a new incoming face by using the estimated initial values.

5. Relation Deriving Suppose the inner face AM is modeled as follows: Suppose the train image number is M, for each the parameter of inner is , and the parameter of the whole is , then we assume that the following linear relationship holds.(i=1,…,M) if we additionally assume that is almost constant, , then the optimal R can be solved by follows: (where , )

6. Relation Deriving Now after obtained the parameter vector fitting the inner face AAM for a new incoming face image, the initial values for the parameter vector fitting the whole face AAM model can be estimated as follows:

7. Multi-band Modeling of Appearance The multiple texture bands can be modeled by simple concatenation. Any correlation between bands is to be picked up by the principal component analysis analogue to the recovered correlation along shape contours. Let m denote the number of texture samples in band i: the concatenated texture vector will then be for p texture bands:

8. The VHE model • V value – The value (intensity) in the HSV colour-space. • H modified hue – The angular hue, h, of an HSV representation modified to accommodate single-band storage. Since faces have little hue variation, the hue circle is here collapsed around the approximate circular mean, and in the following way: • E edge – The edge strength, calculated as the gradient magnitude, where gx and gy are horizontal and vertical gradient images obtained from numeric differential operators with a suitable amount of Gaussian smoothing.

9. Experiments AAM PAAM VHE AAM+ASM VHE for inner

10. Experiments Pt-pt error for each method

11. 谢谢！ 报告人：崔 滢 2011年12月16日