Learning-Based Approach to Estimation of Morphable Model Parameters

Thispaperdescribesamethodforestimatingtheparametersofalinearmorphablemodel(LMM)that models mouth images. The method uses a learning-based approach to estimate the LMMparameters directly from the images of the object class (in this case mouths). Thus this methodcan be used to bypass current computationally intensive methods that use analysis by synthesis,for matching objects to morphable models. We have used the invariance properties of Haarwavelets for representing mouth images. We apply the robust technique of Support VectorMachines (SVM) for learning a regression function from a sparse subset of Haar coefficients tothe LMM parameters. The estimation of LMM parameters could possibly have application toother problems in vision. We investigate one such application, namely viseme recognition.