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Department of Statistics, UCLA

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A High Resolution Grammatical Model for Face Representation and Sketching


In this paper we present a generative, high resolution face representation which extends the wellknown active appearance model (AAM)[5], [6], [7] with two additional layers. (i) One layer refines the global AAM (PCA) model with a dictionary of learned face components to account for the shape and intensity variabilities of eyes, eyebrows, nose and mouth. (ii) The other layer divides the face skin into 9 zones with a learned dictionary of sketch primitives to represent skin marks and wrinkles. This model is no longer of fixed dimensions and is flexible for it can select the diverse representations in the dictionaries of face components and skin features depending on the complexity of the face. The selection is modulated by the grammatical rules through hidden "switch" variables. Our comparison experiments demonstrate that this model can achieve nearly lossless coding of face at high resolution (256 x 256 pixels) with low bits. We also show that the generative model can easily generate cartoon sketches by changing the rendering dictionary. Our face model is aimed at a number of applications including cartoon sketch in non-photorealistic rendering, super-resolution in image processing, and low bit face communication in wireless platforms.

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